Stitch AI

What is Stitch AI's MCP Server?

Stitch AI's MCP Server is a decentralized knowledge hub designed for AI agents. It implements the Model Context Protocol (MCP) to provide a robust system for creating, retrieving, and managing AI agent memories. This allows AI agents to have persistent and organized knowledge bases.

How to Use Stitch AI's MCP Server?

To use Stitch AI's MCP Server, you primarily interact with it through its provided tools. The server can be run locally and integrated with applications like Claude Desktop.

Running the Server:

npm run start

Integration with Claude Desktop:

1. Clone the repository: git clone https://github.com/StitchAI/stitch-ai-mcp.git
2. Install dependencies: npm install @modelcontextprotocol/sdk zod and npm install -D @types/node typescript
3. Install Claude for Desktop.
4. Configure Claude for Desktop by editing the claude_desktop_config.json file (located in ~/Library/Application Support/Claude/ on macOS or %AppData%\Claude\ on Windows) to include your MCP server configuration, specifying the command to run the server and any necessary environment variables like API_KEY and BASE_URL.
5. Restart Claude for Desktop to see the MCP UI elements.

Key Features of Stitch AI's MCP Server

• Memory Space Management:
  • create_space: Create new memory spaces with specified names and types.
  • delete_space: Delete existing memory spaces.
  • get_all_spaces: Retrieve a list of all available memory spaces.
• Memory Management within Spaces:
  • upload_memory: Upload new memories (messages and content) to a specified memory space.
  • get_memory: Retrieve a specific memory by its ID from a memory space.
  • get_all_memories: Retrieve all memories from a specified memory space, with optional filtering by memory names, limit, and offset.
• Decentralized Knowledge Hub: Facilitates a distributed approach to AI knowledge management.
• Model Context Protocol (MCP) Implementation: Adheres to a standard protocol for AI memory management.

Use Cases of Stitch AI's MCP Server

• AI Agent Memory: Provides a foundational layer for AI agents to store, retrieve, and manage their operational and learned memories.
• Contextual AI Interactions: Enables AI systems to maintain context across interactions by accessing relevant past memories.
• Knowledge Base for AI: Serves as a dynamic knowledge base that AI agents can query and update.
• Integration with AI Applications: Designed for integration with AI applications like Claude Desktop to enhance their memory capabilities.

FAQ from Stitch AI's MCP Server

• What is MCP? MCP stands for Model Context Protocol, a protocol for managing AI agent memories.
• How do I configure the server with Claude Desktop? You need to edit the claude_desktop_config.json file in your Claude Desktop application support directory to point to the Stitch AI MCP server and provide necessary API keys and base URLs.
• Can I filter memories when retrieving them? Yes, get_all_memories supports filtering by memory_names as well as pagination parameters like limit and offset.

Stitch AI's MCP Server

Decentralized Knowledge Hub for AI

This repository contains a Model Context Protocol (MCP) server implementation for Stitch AI's memory management system. The server provides tools for creating, retrieving, and managing AI agent memories.

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Available Tools

The MCP server provides the following tools:

create_space

Creates a new memory space with the specified name.

• Parameters:
  • space_name: The name of the memory space to create
  • type: The type of memory space to create

delete_space

Deletes a memory space with the specified name.

• Parameters:
  • space_name: The name of the memory space to delete

get_all_spaces

Gets a list of all available memory spaces.

• Parameters: None

upload_memory

Uploads a new memory to a specified memory space.

• Parameters:
  • space: The name of the memory space to upload to
  • message: The memory message to upload
  • memory: The memory content to upload

get_memory

Retrieves a specific memory by ID from a memory space.

• Parameters:
  • space: The name of the memory space
  • memory_id: The ID of the memory to retrieve

get_all_memories

Retrieves all memories from a specified memory space.

• Parameters:
  • space: The name of the memory space to retrieve memories from
  • Optional Parameters:
    • memory_names: Comma-separated list of memory names to filter
    • limit: Maximum number of memories to return (default: 50)
    • offset: Number of memories to skip (default: 0)

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Run the server

npm run start

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Using with Claude Desktop

1. Clone the repository

   git clone https://github.com/StitchAI/stitch-ai-mcp.git
   

2. Install dependencies

   npm install @modelcontextprotocol/sdk zod
   npm install -D @types/node typescript
   

3. Install Claude for Desktop

   • Download and install the latest version from Claude's website

4. Configure Claude for Desktop

   • Locate your Claude for Desktop configuration file:
     • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
     • Windows: %AppData%\Claude\claude_desktop_config.json
   • Create the file if it doesn't exist

5. Edit Configuration File

   • Open the configuration file in a text editor:
     • macOS: code ~/Library/Application\ Support/Claude/claude_desktop_config.json
     • Windows: code $env:AppData\Claude\claude_desktop_config.json
   • Add your MCP server configuration:

{
    "mcpServers": {
        "stitchai": {
            "command": "npx",
            "args": [
                "ts-node",
                "/path/to/cloned/stitch-ai-mcp/src/server.ts"
            ],
            "env": {
                "API_KEY": "<STITCH_AI_API_KEY>",
                "BASE_URL": "https://api-demo.stitch-ai.co"
            }
        }
    }
}

6. Restart Claude for Desktop
   • After saving the configuration file, restart Claude for Desktop
   • The MCP UI elements will appear in Claude for Desktop once at least one server is properly configured

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Contact

https://x.com/StitchAI_hq