### 1. What "MCP tools for Claude" actually means
In your Liminality project, this phrase describes how the Model Context Protocol (MCP) lets Claude talk to your server. It is the bridge.
* **The Standard:** MCP is an open protocol. It gives AI agents a universal way to reach external tools and data. It separates the agent from the specific integrations. * **Your Role:** For Liminality, the "MCP tools" are the functions, data, and instructions your server exposes. Claude uses these to do things like enrich CRM data or manage databases. You don't write custom code for every single integration. * **What is Exposed:** * **Tools:** Actions the AI can take, such as running a database query or calling an API. * **Resources:** Data the AI can read, like file contents. * **Prompts:** Saved instructions for common tasks.
### 2. How the mechanism works
The system relies on three parts: Host, Server, and Client.
* **The Components:** * **Host:** The app running the LLM. For you, this is where the user talks to Claude. * **Server:** This is Liminality. It exposes the tools and data. It connects the AI to outside systems like Acme CRM, Airtable, or Asana. * **Client:** The piece of the Host that talks to your Server.
* **The Flow:** 1. **Connect:** Claude links to your Liminality MCP Server. 2. **Discover:** The server lists what it can do. 3. **Call:** If the AI needs to migrate a Postgres database, it uses MCP to call the specific tool your server offers. 4. **Validate:** Your server checks the input using SDKs. You can use Python with FastMCP/Pydantic or TypeScript with McpServer/Zod. Then it runs the function. 5. **Return:** The result goes back to Claude, which shows it to the user.
* **Why this helps:** * **Portability:** Agents use the same tools across different hosts. No code rewriting. * **Efficiency:** Offloading data retrieval to external tools keeps the AI's context window clean. It focuses on reasoning, not storing raw data. * **Specialization:** Agents stay specialized while connecting to enterprise systems like GitHub or Salesforce.
### Next Steps
You are building Liminality as a question-decomposition and routing MCP server. To move forward with the "MCP tools for Claude" side, choose one:
1. **Define specific tools** for Liminality (e.g., decomposing questions, routing to Acme CRM, or Gmail notifications). 2. **Check SDK details** for Python or TypeScript to ensure alignment with Anthropic specs. 3. **Explore integration** with your toolkits (Airtable, Asana, Figma) to expose them as MCP Resources or Tools.
Tell me which direction to take or add any constraints for your implementation.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai