mcp-servers-deep-research

# mcp-servers-deep-research Thanks for clarifying. I’ll research the most recent articles, tutorials, and publications (from 2024–2025) related to building and using MCP servers for LLM integration, with a focus on Windsurf and Cursor. I’ll compare their capabilities and suitability for handling local markdown corpora and Airtable data via REST API. I’ll also explore whether local syncing or mirroring of large Airtable bases is a feasible alternative to the REST API, especially for scenarios involving tens of thousands of records. I’ll get back to you shortly with a comparison, architectural outline, and actionable next steps tailored to your technical background. # Introduction to Multi-Context Prompting (MCP) in LLM Tools Large Language Model (LLM) applications often need to draw on multiple context sources – for example, local documents, databases, or external APIs – to provide rich, relevant answers. **Model Context Protocol (MCP)** has emerged (circa 2024) as a standard to enable this “multi-context prompting” via a client–server architecture ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=The%20Model%20Context%20Protocol%20,core%20architectural%20components%20and%20concepts)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=MCP%20uses%20a%20client,handshake%2C%20which%20structures%20the%20exchange)). In this setup, an LLM-powered *client* (such as an AI IDE or chatbot) can connect to one or more MCP *servers*, each server providing access to specific data or tools. Two popular AI coding environments, **Cursor** and **Windsurf**, have adopted MCP to extend their capabilities. Both are VS Code–based IDEs that integrate LLM assistants and support “agent” modes for multi-step tasks ([Windsurf vs Cursor: which is the better AI code editor?](https://www.builder.io/blog/windsurf-vs-cursor#:~:text=match%20at%20L699%20Cline%20is,its%20own%20issues%2C%20and%20continue)) ([Windsurf vs. Cursor - which AI coding app is better?](https://www.thepromptwarrior.com/p/windsurf-vs-cursor-which-ai-coding-app-is-better#:~:text=Windsurf%20step%20by%20step)). This comparison will explore how Cursor and Windsurf (2024–2025 versions) handle multi-context prompting—particularly focusing on: - **Handling Large Local Corpora:** e.g. indexing thousands of Markdown files for context. - **Interfacing with External APIs (Airtable):** how to fetch or sync data from an Airtable base. - **Plugin/Tool Extensibility:** the ease of adding custom tools via MCP. - **Developer Experience:** building MCP servers (FastAPI vs Node.js, etc.) and integrating with these IDEs. - **Latency & Performance:** how each manages speed with multiple context sources. Finally, we provide a practical outline for building an MCP server that unifies local files and Airtable data for use with Cursor or Windsurf. # Handling Large Local Corpora (Knowledge Bases) Integrating a large local knowledge base (thousands of Markdown files or PDFs) as context for an LLM requires efficient search and retrieval. **Both Cursor and Windsurf rely on MCP servers** to provide this capability in a standardized way ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=Imagine%20a%20world%20where%20you,plug%20directly%20into%20your%20apps)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=MCP%20uses%20a%20client,handshake%2C%20which%20structures%20the%20exchange)). There are already MCP integrations designed for local files and documents: - *File System Access:* The official **Filesystem** MCP server can expose read/search operations on local files with access controls ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=Data%20and%20file%20systems)). This could handle simple file queries (open/read a file by path). - *Markdown Knowledge Bases:* There is a dedicated **Obsidian Notes** MCP server (community-supported) that can *“read and search through Markdown notes in Obsidian vaults,”* effectively indexing a folder of Markdown files ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Obsidian%20Markdown%20Notes%20,with%20the%20Tinybird%20serverless%20ClickHouse)). This is ideal for large collections (10,000+ markdown files) as it allows full-text search across the vault. - *Semantic Vector Search:* For more semantic retrieval, connectors exist for vector databases like **Qdrant** or **Weaviate**, which provide *“semantic memory”* and Retrieval-Augmented Generation (RAG) capabilities ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Obsidian%20Markdown%20Notes%20,with%20the%20Tinybird%20serverless%20ClickHouse)). These can store embeddings of your documents and let the LLM find relevant chunks by meaning, not just keywords. **Windsurf** offers additional out-of-the-box support for large codebases, which hints at how it might handle corpora. Its “Context Awareness” features include **Local Indexing**, automatically analyzing your project files to fetch relevant context for the AI agent ([Windsurf vs. Cursor - which AI coding app is better?](https://www.thepromptwarrior.com/p/windsurf-vs-cursor-which-ai-coding-app-is-better#:~:text=In%20Cursor%2C%20you%20usually%20have,is%20pretty%20cool%20and%20intuitive)). In practice, Windsurf’s agent (Cascade) will automatically select the right files or functions needed for a query, whereas early versions of Cursor required the user to manually tag or provide context files ([Windsurf vs. Cursor - which AI coding app is better?](https://www.thepromptwarrior.com/p/windsurf-vs-cursor-which-ai-coding-app-is-better#:~:text=In%20Cursor%2C%20you%20usually%20have,is%20pretty%20cool%20and%20intuitive)). (Recent Cursor updates have introduced an agent mode to bridge this gap ([Windsurf vs. Cursor - which AI coding app is better?](https://www.thepromptwarrior.com/p/windsurf-vs-cursor-which-ai-coding-app-is-better#:~:text=Windsurf%20step%20by%20step)).) For non-code corpora like knowledge bases, however, both tools rely on MCP-based solutions rather than any built-in index. In other words, you would run a separate indexing service (like an Obsidian vault search or a vector DB server) and connect it to either IDE as an MCP plugin. **Scaling to tens of thousands of documents** is made feasible by these approaches. For example, one developer integrated a LanceDB vector index with MCP to let Claude (another LLM client) converse with a PDF library. By using a two-stage retrieval – first searching a *catalog* of documents, then searching within the top matches – the system efficiently handles a large corpus with improved relevance and fewer hallucinations ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=4,reserves%20this%20for%20specific%20scenarios)). This kind of *agentic RAG* (where the LLM decides how to query the index) could equally be used in Cursor or Windsurf. The MCP server can guide the LLM to first pick relevant files, then retrieve specific chunks, optimizing the context passed into the model ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=The%20system%20consists%20of%20several,key%20components)) ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=4,reserves%20this%20for%20specific%20scenarios)). The result is that even a very large local corpus can be harnessed effectively within the LLM’s context window, using multiple focused prompts instead of one gigantic prompt. The trade-off is some added latency for iterative searching, but significantly better accuracy ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=4,reserves%20this%20for%20specific%20scenarios)). In summary, **both Cursor and Windsurf are capable of handling large local corpora via MCP integrations**. Windsurf’s IDE has an edge in *automated context gathering* for code, thanks to its Cascade agent and indexing limits (which increase in the Pro plan) ([Windsurf Editor | Windsurf (formerly Codeium)](https://windsurf.com/editor#:~:text=Premium%20large%20models)). But for arbitrary text documents, you would set up a similar MCP server for either tool – for example, running `npx mcp-obsidian` to serve a vault of Markdown files to the AI ([GitHub - smithery-ai/mcp-obsidian: A connector for Claude Desktop to read and search an Obsidian vault.](https://github.com/calclavia/mcp-obsidian#:~:text=Obsidian%20Model%20Context%20Protocol)) ([GitHub - smithery-ai/mcp-obsidian: A connector for Claude Desktop to read and search an Obsidian vault.](https://github.com/calclavia/mcp-obsidian#:~:text=To%20install%20Obsidian%20Model%20Context,Claude%20Desktop%20automatically%20via%20Smithery)). Once connected, the LLM can call tools like `searchVault(query)` or `readDocument(name)` provided by that server. This allows querying *10,000+ files* in seconds and only feeding the most relevant snippets into the prompt. # Interfacing with External APIs (Airtable) Connecting an LLM to external services like Airtable is another use-case for multi-context prompting. Airtable is a cloud database with a REST API. Neither Cursor nor Windsurf has a built-in Airtable integration, but **MCP makes it possible to create one** or use a generic approach. The idea is to give the LLM a *tool* that can query Airtable (or an equivalent data source) on demand ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=unique%20APIs%20provided%20by%20popular,code%20to%20establish%20these%20connections)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=MCP%20uses%20a%20client,handshake%2C%20which%20structures%20the%20exchange)). There are a couple of strategies to achieve this: **1. Direct API Access via MCP:** You can implement an MCP server that calls the Airtable REST API. For example, a custom tool like `lookupRecord(base, table, query)` could use Airtable’s API (with an API key) to fetch matching records when invoked by the LLM. This is analogous to existing MCP servers for other SaaS APIs – e.g. the official Stripe MCP server lets an AI *“interact with the Stripe API”* for payments ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Prisma%20,the%20Tinybird%20serverless%20ClickHouse%20platform)). The MCP server acts as an intermediary bridging the IDE and Airtable, so the LLM never calls Airtable directly but through the standardized protocol ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=MCP%20uses%20a%20client,handshake%2C%20which%20structures%20the%20exchange)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=an%20MCP%20client%20component%2C%20while,doesn%E2%80%99t%20talk%20to%20APIs%20directly)). One could define resources for base schema or use function arguments for the base/table IDs. **The advantage** is real-time data: the model always gets live Airtable info. **The downsides** are API rate limits and latency. Airtable’s API typically allows ~5 requests per second, and fetching large tables (thousands of records) can be slow. In practice, the LLM would need to ask specific, narrow queries (by record ID or filtered view) rather than pull an entire 50k-record table in one go. **2. Local Sync (Mirroring Airtable Data):** To overcome API limitations, a **viable emerging alternative is to sync Airtable data to a local store** and then expose that to the LLM. This approach is noted for scenarios requiring offline access or heavy read volume ([Synchronizing Airtable with SQLite for Data Management](https://www.w3resource.com/sqlite/snippets/airtable-sqlite-sync.php#:~:text=Airtable%20is%20a%20cloud,based%20and%20local%20data%20synchronization)). For example, one can regularly export each Airtable base to a local SQLite or PostgreSQL database. Airtable itself provides one-way sync between bases, but for our purposes, a custom script or third-party tool (like Whalesync or the Airtable CLI) could maintain a local mirror. Once the data is local, you have many options to serve it to the LLM efficiently: you could use a vector search to find relevant text within the records, or simply enable SQL queries. Notably, **MCP already has official servers for databases** like SQLite and Postgres that could be repurposed here. The official **SQLite MCP server** provides *“database interaction and business intelligence features,”* and the Postgres server allows read-only SQL with schema inspection ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Filesystem%20,search%20capabilities%20for%20Google%20Drive)). In other words, you could load all Airtable records into a SQLite file and then connect the ready-made SQLite MCP server – the AI agent could then query it with natural language (which the server translates to SQL under the hood). This would bypass Airtable’s API during queries, eliminating call latency and rate limits. The data would be as fresh as your last sync; for many use cases, a periodic sync (e.g. every hour) is acceptable. **Local vs API trade-offs:** A local mirror improves speed dramatically and lets you use sophisticated search (full-text or embeddings) across all those records. It is indeed *feasible* even at the scale of 100+ bases × 50k records – modern SQLite can handle millions of records, especially read-only queries. A tutorial by W3resource demonstrates syncing Airtable to SQLite for *“consistent records both online and offline,”* highlighting how this hybrid approach can maintain data alignment between Airtable and a local cache ([Synchronizing Airtable with SQLite for Data Management](https://www.w3resource.com/sqlite/snippets/airtable-sqlite-sync.php#:~:text=Airtable%20is%20a%20cloud,based%20and%20local%20data%20synchronization)). The main considerations are ensuring the sync process is robust (so the local DB doesn’t go stale or diverge) and acknowledging that very fresh changes in Airtable might not be immediately reflected locally. If that is a concern, one can always fall back to an API call for the absolutely latest data on a specific record. From a **Cursor/Windsurf perspective**, both approaches appear the same to the IDE: you would register an MCP server that either (a) calls Airtable API on the fly or (b) queries the local mirror. The LLM agent just sees a “AirtableTool” in its toolkit. For example, you might have a tool defined as `getCustomerInfo(name)`, and under the hood it either does an API GET request or looks up a local SQL table. **The MCP framework ensures the integration is seamless** – the AI issues a function call, and your server returns data in a standard JSON format that the IDE presents to the model ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=The%20server%20exposes%20capabilities%20,handshake%2C%20which%20structures%20the%20exchange)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=instead%2C%20it%20goes%20through%20the,handshake%2C%20which%20structures%20the%20exchange)). This abstraction is powerful because it means switching from direct API to a local DB (or vice-versa) doesn’t require changing Cursor or Windsurf at all, only the server’s implementation. The **bottom line** is that both Cursor and Windsurf can interface with Airtable data, but given the scale (potentially millions of records), a local syncing approach is increasingly appealing for performance and context window reasons. # Plugin and Tool Extensibility via MCP One of the strengths of MCP is that it turns external integrations into **plug-and-play modules**. Both Cursor and Windsurf fully embrace this extensible plugin system. In fact, MCP is often described as *“a universal connector for AI”* or a *“plugin system for Cursor”* ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=Model%20Context%20Protocol%20,context%20and%20tools%20to%20LLMs)) ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=that%20standardizes%20how%20applications%20provide,context%20and%20tools%20to%20LLMs)). In practical terms, this means you can **extend the AI’s capabilities by simply running or installing new MCP servers**, without modifying the IDE’s code. There is already a **growing ecosystem of MCP servers** covering a wide range of tools and data sources. Official and community-maintained servers exist for everything from Git operations to cloud APIs. For example, you can connect an AI to Slack or Gmail for messaging, to GitHub for code repo management, to Stripe for payments, or to Notion/Confluence for document search ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=For%20example%2C%20an%20MCP%20server,notes%20from%20your%20Obsidian%20vault)) ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=%E2%86%92%20Send%20emails%20through%20Gmail,%E2%86%92%20Update%20records%20in%20Salesforce)). A list of official integrations (maintained by various companies) includes tools like Axiom (log query), BrowserStack, Cloudflare, as well as data-focused ones like the Obsidian and database connectors mentioned earlier ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20E2B%20,for%20search%2C%20crawling%2C%20and%20sitemaps)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Obsidian%20Markdown%20Notes%20,with%20the%20Tinybird%20serverless%20ClickHouse)). Community-driven servers go even further – there are MCP plugins for Docker, Kubernetes, Linear, Spotify, and many more niches ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=Community%20highlights)). This demonstrates how quickly the “app store” of MCP tools is expanding. **Cursor** added MCP support in late 2024 (version 0.45.6) and provides a UI in settings to manage servers ([Cursor added MCP support in 0.45.6 - Reddit](https://www.reddit.com/r/cursor/comments/1idgbqu/cursor_added_mcp_support_in_0456/#:~:text=Cursor%20added%20MCP%20support%20in,pass%20ENV%20vars%20in)). You can enable MCP, then add servers either by selecting from a list or specifying a custom command/URL. Recent guides show how to connect Cursor to “100+ fully managed MCP servers within minutes” using services like Composio (which hosts many integrations with built-in auth) ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=I%20will%20be%20using%20Composio,comes%20with%20fully%20managed%20servers)) ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=Think%20of%20it%20as%20a,various%20data%20sources%20and%20tools)). In Cursor’s case, you can even pass environment variables (API keys, etc.) through the config for each server ([Cursor added MCP support in 0.45.6 - Reddit](https://www.reddit.com/r/cursor/comments/1idgbqu/cursor_added_mcp_support_in_0456/#:~:text=Cursor%20added%20MCP%20support%20in,pass%20ENV%20vars%20in)). **Windsurf** likewise has native MCP integration: in its Cascade settings, there’s an **“Add Server”** interface that lists popular servers and allows custom ones ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Adding%20a%20new%20server)) ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=can%20then%20easily%20choose%20between,mcp_config.json)). Windsurf supports both local process (`stdio`) servers and networked (`/sse`) servers for MCP ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Windsurf%20supports%20two%20transport%20types,sse)). This means you can either run a tool locally (the IDE spawns it as a child process) or connect to a remote URL serving the MCP protocol. Windsurf’s polished UI for MCP (including a JSON config viewer and toggle to enable/disable servers) makes it straightforward to toggle integrations. **Extensibility comparison:** In terms of raw capability, **Cursor and Windsurf are on par** – both adhere to the MCP standard, so any MCP-compliant server will work with either client. A tool developed for Cursor is equally usable in Windsurf, Claude Desktop, or others, thanks to the common protocol. The difference lies in developer experience and ecosystem at this point in time. Windsurf’s approach is very user-friendly, surfacing the configuration in a GUI and providing some pre-populated options ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Adding%20a%20new%20server)). Cursor’s interface is improving, but some users initially found it a bit clunky (especially on Windows, where editing a global `mcp.json` was required) ([MCP server support for Windows? - Cursor - Community Forum](https://forum.cursor.com/t/mcp-server-support-for-windows/71051#:~:text=MCP%20server%20support%20for%20Windows%3F,json%20file)). That said, with Cursor’s growing popularity, its community and third-party support (like Cursor Directory or marketplace sites) are booming as well. Several marketplaces now list ready-to-use MCP servers (with hosting) – e.g. **Smithery**, **MCP Hub**, **OpenTools**, **Composio**, etc., all offering one-line installation or deployment of tools ([Unlocking AI’s potential: How to quickly set up a Cursor MCP Server](https://www.apideck.com/blog/unlocking-ai-potential-how-to-quickly-set-up-a-cursor-mcp-server#:~:text=,source%20MCP%20Servers%20%2A%20MCP.so)). This means developers don’t even always need to write their own integrations; they can search for an existing plugin. As one blog succinctly put it: *“MCP servers serve as lightweight programs that expose specific capabilities... acting as intermediaries between Cursor and various external tools or data sources.”* This lets you **“plug in new capabilities without custom-coding each from scratch” — like a library of ready-made plugins that all speak the same language ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=,external%20tools%20or%20data%20sources))**. From the perspective of an end-user or developer using the IDEs, **extending Cursor or Windsurf via MCP is highly flexible**. You can start the IDE, connect it to, say, a *Memory* server (vector memory), a *Filesystem* server, and a *Web fetch* server all at once. The LLM inside then automatically gains the ability to use those tools in one conversation. This enables complex workflows that span multiple contexts (e.g., “Search my docs, then take that info and update a record in Airtable, then send an email”), all orchestrated by the AI. Such multi-step agent capabilities are exactly what MCP was designed for, and both IDEs have become capable platforms for them. # Developer Experience (Building MCP Servers: FastAPI vs Node.js) For developers looking to **build their own MCP server**, the good news is that there are robust SDKs and multiple language options. Official SDKs are available in **Python, TypeScript/Node.js, Java, Kotlin, and C#** ([Building MCP with LLMs - Model Context Protocol](https://modelcontextprotocol.io/tutorials/building-mcp-with-llms#:~:text=,SDK)), covering a wide range of developer preferences. The choice between Python and Node.js often comes down to the task at hand and the developer’s familiarity: - **Node.js / TypeScript:** The TypeScript SDK allows you to publish servers as npm packages, which others can install via a simple `npx` command. Many reference servers (like the Filesystem and GitHub examples) are implemented in TS. Using Node can be advantageous for easier distribution (e.g. `npx @modelcontextprotocol/server-github` runs a GitHub tool instantly ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=TypeScript,npx))) and integration with JavaScript-based APIs or libraries. If your context source has a nice Node.js API (for example, Airtable has a JS client library), building with Node might speed things up. Node’s event-driven nature also fits well with the MCP concept of handling requests from the client asynchronously (especially if using the SSE transport). - **Python:** The Python SDK is equally capable and is often chosen for data-heavy or ML-heavy tasks. If you plan to leverage Python’s ecosystem (pandas for data, LangChain for retrieval, PyTorch/TensorFlow for any ML tasks, etc.), a Python MCP server makes sense. You can run Python-based servers as standalone programs or even as web services. For instance, the official guide shows you can install a Python server via `pip` and run it (`python -m mcp_server_name`) or use a tool like `uvicorn` (`uvx`) to run it with async support ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=TypeScript,npx)). Python’s FastAPI or Flask can integrate an MCP **SSE endpoint**: since MCP over HTTP is basically a Server-Sent Events stream, you could mount the MCP handler at `/sse`. In fact, Windsurf’s docs indicate that to connect to an HTTP server, you point it to `https://your-server-url/sse` ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Windsurf%20supports%20two%20transport%20types,sse)). This implies you can develop an MCP server as a web service (with FastAPI’s `EventSourceResponse` for SSE, for example), which might be convenient for deploying centrally. - **Framework integration:** Regardless of language, building an MCP server typically involves defining *Tools* and *Resources*. Tools are actions/functions the LLM can invoke; Resources are data context to fetch (like documents or database entries) ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=Understanding%20the%20Connection%20Between%20Clients%2C,Servers%2C%20and%20LLMs)) ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=,requests%20from%20the%20MCP%20Client)). The SDKs simplify this: for example, in Python you might use a decorator `@mcp.tool` on a function to expose it. In Node/TS, you might register functions in a server class. You can certainly wrap complex logic or external library calls inside these tool functions. If using FastAPI, you could have an initialization that loads data (e.g., reads all markdown files into an index) when the server starts, then the tool functions query that in-memory index. With Node, you might do similarly using an Express server or just a CLI app that uses stdio. - **Debugging and testing** is aided by tools like the MCP Inspector and logging options ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=,Inspector)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=)). From a dev experience standpoint, being able to iterate quickly is key. Some developers even use an LLM to help write the MCP server itself – Anthropic’s Claude and others have been used to draft MCP server code given a description of the desired functionality ([Building MCP with LLMs - Model Context Protocol](https://modelcontextprotocol.io/tutorials/building-mcp-with-llms#:~:text=Describing%20your%20server)) ([Building MCP with LLMs - Model Context Protocol](https://modelcontextprotocol.io/tutorials/building-mcp-with-llms#:~:text=For%20example%3A)). Both Cursor and Windsurf treat external MCP servers similarly, so there’s no significant difference in *deploying* your server for one or the other. **Cursor’s config** allows specifying a local command or an HTTP(S) URL for the server, along with any required env vars ([Cursor added MCP support in 0.45.6 - Reddit](https://www.reddit.com/r/cursor/comments/1idgbqu/cursor_added_mcp_support_in_0456/#:~:text=Cursor%20added%20MCP%20support%20in,pass%20ENV%20vars%20in)). **Windsurf’s config** (in the GUI or JSON) does the same ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Scroll%20down%20to%20the%20Cascade,mcp_config.json)). One nuance: Windsurf explicitly mentions only `stdio` and `/sse` transports ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Windsurf%20supports%20two%20transport%20types,sse)), while Cursor (being based on the same MCP spec) supports those too; `stdio` is great for a local FastAPI server started via command, and `/sse` is used if you host it separately. In terms of **performance of the server**, Python vs Node might not differ greatly unless you’re doing heavy compute in the tool. If your MCP server is mostly I/O bound (reading files, calling an API, querying a DB), both languages can handle multiple concurrent requests fairly well (Node via its event loop, Python via async or threads). The MCP protocol itself is lightweight (built on JSON-RPC 2.0 over streams or SSE) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=on%20JSON,standardized%20%E2%80%9Clanguage%E2%80%9D%20for%20all%20interactions)). Most devs choose the environment that aligns with their data source: e.g. use Python for a local ML model or data science stack, or use Node for quick scripting and sharing. Importantly, *both* SDKs are being actively updated, and you can find plenty of examples to start from ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=These%20official%20reference%20servers%20demonstrate,MCP%20features%20and%20SDK%20usage)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=Getting%20started)). Developer communities (on Discord, forums) are also available for both Cursor and Windsurf to help troubleshoot MCP server creation. To summarize the developer experience: **it’s quite friendly and flexible**. You’re not locked into one language or framework. FastAPI can be used to create a robust HTTP MCP service if you prefer Python, or you can whip up a Node script and distribute it via npm. Thanks to MCP’s standardized interface, once your server is running, **any MCP-compatible client can use it immediately** ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=,standardized%20protocol%2C%20acting%20as%20intermediaries)) ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=,world%20tools%20and%20data)). This reduces the burden on developers – instead of writing bespoke plugins for Cursor and another for Windsurf, you write one MCP server that works for both. The focus then shifts to *what* you want to do (e.g., search 10k Markdown files, or fetch from Airtable) rather than *how* to integrate with each app. # Latency and Performance Characteristics When combining multiple context sources, performance considerations become important. We can look at this on two levels: **(a)** the overhead of using MCP tools in the conversation, and **(b)** any differences between Cursor and Windsurf in how they handle prompting and tool use. **MCP overhead:** Using external tools will naturally incur some latency – e.g., a round-trip to your MCP server to fetch data. If the server is local and fast (like an in-memory search), this might be on the order of tens of milliseconds. If it’s calling a remote API or doing heavy computation, it could be a few seconds. The protocol overhead itself (JSON serialization and communication) is minimal compared to the action being performed. Both Cursor and Windsurf process tool responses asynchronously, meaning the LLM “waits” for the result before continuing the conversation. One way to mitigate latency is to **pre-fetch or cache results**. For instance, if you know a user will frequently query a certain subset of documents, your MCP server could cache embeddings or query results in memory. Another approach is batching: some MCP servers allow the LLM to request multiple items in one call if that’s foreseen by the tool design (reducing multiple round-trips). One thing to note is that giving the LLM access to a large context doesn’t mean it will always use it optimally. As observed in the Claude+LanceDB experiment, the LLM sometimes performed multiple small searches rather than one big query ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=4,reserves%20this%20for%20specific%20scenarios)). This *adaptive querying* improved answer quality but also adds a bit of time. So there is a trade-off between **latency and completeness**. In practice, users can configure how many tools they connect; adding more MCP servers might increase overall latency if the AI tries many tools in sequence. It’s often wise to start with just the integrations you need for a task to keep the agent focused (and fast). **Cursor vs Windsurf (performance):** Both IDEs leverage similar model backends (often Anthropic Claude or OpenAI models) and the same MCP mechanism, so one wouldn’t expect a huge difference in raw execution time for a given tool. However, early user reports indicate that **Windsurf’s agent felt snappier in responsiveness**. In one head-to-head test using Claude 3.5 as the model, *“Windsurf felt faster both in generating responses and getting things done”*, whereas *“there is a perceptible lag in Cursor”* when invoking the agent on the same task ([Windsurf vs Cursor — Initial Thoughts | GuardianeLinks - Форум по программированию на Delphi](https://www.guardianelinks.com/threads/windsurf-vs-cursor-initial-thoughts.62942/#:~:text=Windsurf%20is%2Ffeels%20faster)). These observations suggest that Windsurf’s implementation of request handling and UI update might be more optimized or simply that its Cascade agent pipelines requests more efficiently. Cursor was by no means slow, but the slight delay was noticeable. It’s worth noting that the **output quality was similar** between the two in that test ([Windsurf vs Cursor — Initial Thoughts | GuardianeLinks - Форум по программированию на Delphi](https://www.guardianelinks.com/threads/windsurf-vs-cursor-initial-thoughts.62942/#:~:text=With%20Claude%203,a%20perceptible%20lag%20in%20Cursor)) – Cursor didn’t use the extra time to produce a better answer, so the lag seemed like pure overhead. One reason for Windsurf’s speed could be its tighter integration of the agent with the IDE state. It actively tracks your open files, edits, and even runs the terminal for you as part of its flow ([Windsurf Editor | Windsurf (formerly Codeium)](https://windsurf.com/editor#:~:text=Cascade)) ([Windsurf Editor | Windsurf (formerly Codeium)](https://windsurf.com/editor#:~:text=Suggest%20and%20run%20commands)). This *“full contextual awareness”* might allow it to pre-emptively load relevant context or handle tool outputs faster. Cursor, on the other hand, initially took a more cautious approach (requiring user approval to apply code changes, etc., which slows down the cycle) ([Windsurf vs Cursor — Initial Thoughts | GuardianeLinks - Форум по программированию на Delphi](https://www.guardianelinks.com/threads/windsurf-vs-cursor-initial-thoughts.62942/#:~:text=Windsurf%27s%20,a%20smaller%20frame%20is%20hard)). As both tools evolve, Cursor has been catching up in agent features and likely optimizing performance as well. For non-coding use (like pure Q&A over documents), the difference may be negligible, especially if using the same model via API. **Latency in data retrieval:** If your MCP server needs to search 10,000 files or query a 50k-row database, that process itself must be efficient to avoid bottlenecks. Using indexes (in-memory maps or vector indices) is crucial. The Obsidian vault plugin presumably indexes notes on startup so that a search query is fast. Similarly, a database query with proper indices (or a vector search with ANN algorithms) can retrieve results in under a second even from large data. So, when building your multi-context solution, ensure each component is as optimized as possible (you might use **embedding caching**, or limit scope by requiring the AI to pick a specific base before searching, etc., to reduce load). Both Cursor and Windsurf allow you to set a *timeout* for tool responses – if a tool takes too long, the model can continue without it, to avoid hanging. This is an important safety for latency: if Airtable’s API is slow one moment, the AI won’t freeze indefinitely. In terms of **concurrency**, if multiple tools are triggered by the AI in parallel, the clients handle it sequentially by default (the LLM will call one function at a time in current implementations). So you won’t usually saturate your CPU with many simultaneous calls. But heavy workflows could involve a sequence of tool uses: e.g., search documents, then call an API, then use a calculator. Each step adds to total latency. The design goal, though, is that these multi-step operations are still far faster than a human switching between these apps manually. To conclude on performance: **Windsurf has been noted to have a slight speed edge in agent responsiveness** ([Windsurf vs Cursor — Initial Thoughts | GuardianeLinks - Форум по программированию на Delphi](https://www.guardianelinks.com/threads/windsurf-vs-cursor-initial-thoughts.62942/#:~:text=Windsurf%20is%2Ffeels%20faster)), but both platforms are actively optimizing. The main performance factors lie in how you implement and host your context-providing servers. Choosing local data stores or batched queries can significantly improve throughput versus hitting remote APIs repeatedly. With careful design, a multi-context prompting server can remain well within acceptable latency for interactive use – often a few seconds or less for complex queries – while dramatically expanding the AI’s capabilities. # Practical Outline: Building an MCP Server for Local Files + Airtable Finally, let’s outline how one could build a custom MCP server that **combines both local markdown files and Airtable data**, and deploy it for use with Cursor or Windsurf. This high-level plan assumes you want the AI to be able to answer questions using information from a folder of documents and from Airtable bases: **1. Environment Setup:** Decide on the implementation language. For example, **Python + FastAPI** is a good choice if you plan to do text processing or want easy Airtable API use (via the `requests` library). Alternatively, **Node.js** could be used if you prefer JavaScript (there’s an official Airtable JS client and the MCP TypeScript SDK). Install the MCP SDK for your language ([Building MCP with LLMs - Model Context Protocol](https://modelcontextprotocol.io/tutorials/building-mcp-with-llms#:~:text=,SDK)). Also gather credentials (e.g., your Airtable API key) and any config (like the path to your markdown directory, and a list of Airtable base IDs to use). **2. Index Local Markdown Files:** Implement a module that scans the markdown folder and builds an index. This could be as simple as reading all files into memory or as advanced as creating embeddings for each file/chunk. For a basic approach, you might create a dictionary of `{filename: content}` and perhaps a search function that does keyword matching. For larger scale (10k+ files), consider integrating a search library or a small vector database. (You could leverage existing solutions; for instance, using **LanceDB or Chroma** in Python to store embeddings ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=1,employs%20document%20hashing%20to%20avoid)), or even use the existing **Obsidian MCP** server as inspiration, since it already handles reading/searching many markdown notes.) **3. Implement Airtable Access:** Write a utility to fetch data from Airtable. Using Airtable’s REST API, you’ll likely want functions like `fetch_record(base, table, record_id)` and `search_records(base, table, query)`. Airtable returns JSON; parse the fields you need. Be mindful of rate limits – if this server will be making many calls, you might incorporate caching. If you opt for **local syncing**, you would here set up a routine to pull down all records from targeted tables and store them in a local database (SQLite or even in-memory Python structures). This could run at startup or on a schedule. For our outline, assume real-time API calls for simplicity. **4. Define MCP Tools:** Using the SDK, define the tools that the LLM can invoke: - A tool for document search, e.g. `search_docs(query: str) -> List[str]`. This tool takes a query and returns a list of filenames or summaries that match from the markdown corpus. Under the hood, it uses the index from step 2 (keyword search or vector similarity) to find relevant docs. - A tool for retrieving document content, e.g. `read_doc(filename: str) -> str`. This returns the content (or a snippet) of a specified markdown file. The AI might call this after `search_docs` to get the full text of a candidate file it thinks is relevant. - Tools for Airtable, e.g. `find_record(base: str, table: str, criteria: str) -> str` which performs a search (perhaps using Airtable’s filter formula or a simple iteration in the synced data) and returns matching record(s). You could also have `get_record(base: str, table: str, id: str)` for direct retrieval by ID or name. Design the interface that makes sense for your use case (the AI might not know base IDs unless you map human-friendly names). - Optionally, a tool to summarize or extract from records if they are large, to fit into context windows. Each tool will use the functions from steps 2 and 3 to do the actual work. With the Python SDK, you’d annotate these with `@mcp.tool` and provide a description docstring (this is what the AI sees to understand what the tool does). **5. Run and Test the MCP Server:** Decide how it will run: - For **FastAPI (Python)**: set up an endpoint `/sse` that yields events. The Python MCP SDK might have utilities to integrate with FastAPI; otherwise, you can manually handle the JSON-RPC over SSE. You then run the server (e.g., `uvicorn myserver:app --port 8000`). Windsurf or Cursor will connect to `http://localhost:8000/sse` in this case ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Windsurf%20supports%20two%20transport%20types,sse)). - For **Node.js**: you could make it a CLI (stdio transport). For example, using the TS SDK, you create a Server instance, register the tools, and call `server.startStdio()`. This will listen for JSON-RPC over stdin/stdout. Then you can distribute it via npm. Running `npx my-mcp-server` will start it. In Cursor/Windsurf config, you’d put the command to execute this (as shown in many examples) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=TypeScript,npx)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=,filesystem%22%2C%20%22%2Fpath%2Fto%2Fallowed%2Ffiles%22%5D)). If you prefer an HTTP server in Node, you could use Express with an SSE endpoint similarly. Before hooking into the IDE, test the server standalone. You can simulate a client by sending it a JSON-RPC request (or easier: write a small script to call one of your tool functions and see if the logic works). This ensures your Airtable integration is correct and your file search returns expected results. **6. Integrate with Cursor/Windsurf:** Open your IDE’s settings for MCP servers. In **Cursor**, go to *Settings > Features > MCP Servers* and add a new server. You might name it “KnowledgeBase” and either set the command (`npx my-mcp-server` with any args, or `python myserver.py`) or the URL (`http://localhost:8000/sse`) depending on how you ran it ([Cascade MCP Integration](https://docs.windsurf.com/windsurf/mcp#:~:text=Scroll%20down%20to%20the%20Cascade,mcp_config.json)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=,filesystem%22%2C%20%22%2Fpath%2Fto%2Fallowed%2Ffiles%22%5D)). In **Windsurf**, go to *Cascade settings > Add Server*. If you deployed as a local process, choose that option (maybe “custom server”) and provide the command or path. If it’s remote, paste the URL. Windsurf also allows you to toggle servers on/off easily, so ensure it’s enabled. Both IDEs might prompt you to allow the new tools (for security). Approve your tools, and they should become available to the AI. You can verify in Windsurf’s Cascade panel or Cursor’s agent panel that the tools are listed, often with the description you gave (e.g., “search_docs: search local documents” etc.). **7. Usage and Iteration:** Now try it out! Ask the AI something that requires those contexts, e.g., “Find information about XYZ from my notes and the Airtable CRM.” The AI should decide to call `search_docs` or `find_record` as needed. Watch the tool calls and responses (Cursor and Windsurf typically log them in the chat). If the AI gets confused, you may need to tweak the tool descriptions or provide guidance. For instance, you might add a **Prompt** in MCP terms: a system instruction that explains when to use the file search vs when to use the Airtable tool. (MCP supports predefined prompt injections as well ([Model Context Protocol (MCP) Explained - by Nir Diamant - DiamantAI](https://diamantai.substack.com/p/model-context-protocol-mcp-explained#:~:text=Model%20Context%20Protocol%20,via%20a%20client)) ([ Understanding Model Context Protocol: A Deep Dive into Multi-Server LangChain Integration | by Plaban Nayak | The AI Forum | Mar, 2025 | Medium](https://medium.com/the-ai-forum/understanding-model-context-protocol-a-deep-dive-into-multi-server-langchain-integration-3d038247e0bd#:~:text=Model%20Context%20Protocol%20,and%20act%20autonomously%20over%20time)).) Over time, you can refine your server – maybe add caching of Airtable results, or add a new tool to edit an Airtable record if you want read-write capability. **8. (Optional) Sync Airtable Locally:** If you started with direct API calls but find it slow or hitting limits, implement a background sync as discussed. For example, on server startup, pull all records from key tables and store them in a SQLite DB (or even a simple Python dict if it fits in memory). Then change your `find_record` tool to search that local data. You can use SQL queries via Python’s `sqlite3` module or just loop in memory. The improvement in latency will be noticeable, especially for repeated queries. Keep an endpoint or method to refresh the cache periodically. Following this outline, you will have a running **MCP server that provides multi-context knowledge**: it can fetch from thousands of markdown docs and dozens of Airtable bases on demand. By connecting it to Cursor or Windsurf, your AI assistant in the IDE can draw upon **both** your local knowledge base and your cloud data when answering questions or generating code. This setup exemplifies the power of multi-context prompting – effectively breaking the context window barrier by offloading storage to external tools and pulling in just the information needed, when it’s needed. With MCP and modern AI IDEs, developers are now able to integrate these diverse data sources in a relatively straightforward way, significantly enhancing what the LLM can do ([Model Context Protocol (MCP): A comprehensive introduction for developers ](https://stytch.com/blog/model-context-protocol-introduction/#:~:text=,external%20tools%20or%20data%20sources)) ([How to connect Cursor to 100+ MCP Servers within minutes - DEV Community](https://dev.to/composiodev/how-to-connect-cursor-to-100-mcp-servers-within-minutes-3h74#:~:text=For%20example%2C%20an%20MCP%20server,notes%20from%20your%20Obsidian%20vault)). **Sources:** The information above draws from recent documentation and experiments with MCP (2024–2025). Notable references include the Model Context Protocol official docs ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=%2A%20Obsidian%20Markdown%20Notes%20,with%20the%20Tinybird%20serverless%20ClickHouse)) ([Example Servers - Model Context Protocol](https://modelcontextprotocol.io/examples#:~:text=TypeScript,npx)), insights from developers implementing local PDF and database search via MCP ([True Agentic RAG: How I Taught Claude to Talk to My PDFs using MCP (Model Context Protocol) | by Alexander Komyagin | Medium](https://medium.com/@adkomyagin/true-agentic-rag-how-i-taught-claude-to-talk-to-my-pdfs-using-model-context-protocol-mcp-9b8671b00de1#:~:text=4,reserves%20this%20for%20specific%20scenarios)) ([Synchronizing Airtable with SQLite for Data Management](https://www.w3resource.com/sqlite/snippets/airtable-sqlite-sync.php#:~:text=Airtable%20is%20a%20cloud,based%20and%20local%20data%20synchronization)), and comparative reviews of Windsurf vs Cursor ([Windsurf vs Cursor — Initial Thoughts | GuardianeLinks - Форум по программированию на Delphi](https://www.guardianelinks.com/threads/windsurf-vs-cursor-initial-thoughts.62942/#:~:text=Windsurf%20is%2Ffeels%20faster)) ([Windsurf vs. Cursor - which AI coding app is better?](https://www.thepromptwarrior.com/p/windsurf-vs-cursor-which-ai-coding-app-is-better#:~:text=In%20Cursor%2C%20you%20usually%20have,is%20pretty%20cool%20and%20intuitive)). These sources illustrate the capabilities and trade-offs of building an MCP-based “knowledge server” and using it within AI development tools.