Fireproof MCP Server

What does “Fireproof” MCP Server do?

The Fireproof MCP (Model Context Protocol) Server acts as a bridge between AI assistants and a Fireproof database, enabling seamless storage and retrieval of JSON documents through LLM tool use. It provides a simple yet effective way to implement CRUD (Create, Read, Update, Delete) operations and allows documents to be queried and sorted by any field. This server enhances AI development workflows by allowing assistants to interact programmatically with persistent data, making it easier to manage structured information, automate data-driven tasks, and integrate with external tools or APIs. The Fireproof MCP Server is especially useful in scenarios where AI needs to read or modify data on-the-fly, supporting advanced development and prototyping workflows.

List of Prompts

No prompt templates are mentioned in the repository.

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List of Resources

No explicit MCP resources are described in the available documentation or files.

List of Tools

• CRUD Operations: The server implements basic Create, Read, Update, and Delete operations for JSON documents, allowing AI clients to manage their own structured data within the Fireproof database.
• Query Documents: Allows querying of documents sorted by any field, giving AI clients flexibility in data retrieval and manipulation.

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Use Cases of this MCP Server

• Persistent Data Storage for LLMs: Enable AI assistants to store and retrieve structured JSON documents as part of their workflows, such as saving conversation history, user preferences, or application state.
• Prototyping AI Applications: Rapidly build and test LLM-powered apps that require backend storage without setting up a full database infrastructure.
• Database Management: Use the server to manage, update, and query collections of documents for tasks like project management, note-taking, or inventory tracking.
• Codebase Exploration and Metadata Storage: Store and update metadata or annotations related to codebases, enabling AI agents to keep track of code changes, review notes, or documentation.
• API Integration: Serve as a lightweight backend for integrating external APIs that require persistent storage or logging of results.

How to set it up

Windsurf

1. Ensure Node.js is installed and the Fireproof MCP server code is downloaded.
2. Build the server: npm install and npm build.
3. Locate Windsurf’s configuration file (refer to Windsurf docs).
4. Add the Fireproof MCP server to the config:

   {
     "mcpServers": {
       "fireproof": {
         "command": "/path/to/fireproof-mcp/build/index.js"
       }
     }
   }
   

5. Save the file and restart Windsurf.
6. Verify the server is registered in the MCP server list.

Claude

1. Download and build the Fireproof MCP server: npm install then npm build.
2. Edit the Claude config file:
   • On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • On Windows: %APPDATA%/Claude/claude_desktop_config.json
3. Add the following JSON to the mcpServers object:

   {
     "mcpServers": {
       "fireproof": {
         "command": "/path/to/fireproof-mcp/build/index.js"
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm Fireproof MCP is available.

Cursor

1. Install Node.js and clone the Fireproof MCP repository.
2. Build the server with npm install and npm build.
3. Open Cursor’s MCP server configuration file.
4. Add:

   {
     "mcpServers": {
       "fireproof": {
         "command": "/path/to/fireproof-mcp/build/index.js"
       }
     }
   }
   

5. Save and restart Cursor.

Cline

1. Ensure prerequisites (Node.js).
2. Download and build Fireproof MCP: npm install, npm build.
3. Access Cline’s MCP configuration file.
4. Insert:

   {
     "mcpServers": {
       "fireproof": {
         "command": "/path/to/fireproof-mcp/build/index.js"
       }
     }
   }
   

5. Save, restart, and verify setup.

Securing API Keys

No API keys or environment variables are specified in the repository. If needed, you could secure keys like so:

{
  "mcpServers": {
    "fireproof": {
      "command": "/path/to/fireproof-mcp/build/index.js",
      "env": {
        "API_KEY": "${FIREPROOF_API_KEY}"
      },
      "inputs": {}
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "fireproof": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “fireproof” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on these tables, the Fireproof MCP Database Server is a minimal but functional MCP implementation. It covers the basics (CRUD tools and setup instructions), but lacks explicit prompt templates, resource definitions, and advanced features like roots or sampling support. If you need a lightweight document store for LLMs, it’s a solid starting point, but more documentation and capabilities would improve its score.

MCP Score

Overall rating: 5/10 – It achieves the basics, is open source, and provides practical value, but lacks completeness in documentation and advanced MCP features.