Boost.space MCP Server Integration

What does “Boost.space” MCP Server do?

The Boost.space MCP Server acts as a bridge between AI assistants and the Boost.Space REST API, allowing MCP clients (such as Claude Desktop) to interact with Boost.Space’s platform. By proxying Boost.Space’s API, it enables AI-powered tools to perform various operations like querying databases, managing files, or interacting with external services available through Boost.Space. This enhances developer workflows by centralizing access to organizational resources, automating repetitive tasks, and facilitating seamless integration between AI models and structured business data or workflows.

List of Prompts

No prompt templates are documented in the repository.

List of Resources

No explicit MCP resources are listed or described in the repository.

List of Tools

No tools are described or listed in the repository’s documentation or code index.

Use Cases of this MCP Server

• Boost.Space Data Access: Enables AI agents to retrieve and work with data stored in the Boost.Space platform, facilitating automated analysis or reporting.
• Integration with AI Assistants: Acts as a backend for desktop AI clients (e.g., Claude Desktop) to interact with organizational data securely and efficiently.
• API Proxying: Provides a standardized way for AI clients to access Boost.Space REST API endpoints without direct handling of credentials or HTTP logic.
• Streamlined Developer Workflows: Simplifies connecting LLM-powered interfaces to business operations and data, reducing manual integration effort.

How to set it up

Windsurf

No explicit setup instructions for Windsurf are provided in the repository.

Claude

1. Ensure Python and the required dependencies are installed.
2. Install the Boost.space MCP server:

   pip install boostspace-mcp
   

3. In your Claude Desktop configuration, add the following to your mcpServers object:

   "mcpServers": {
     "boostspace": {
       "command": "python",
       "args": ["-m","boostspace_mcp.server"],
       "env": {
         "BOOSTSPACE_API_BASE": "{{API_PATH}}",
         "BOOSTSPACE_TOKEN": "{{TOKEN}}"
       },
       "transport": "stdio"
     }
   }
   

4. Restart Claude Desktop to load the new MCP server.
5. Set your API keys (BOOSTSPACE_API_BASE and BOOSTSPACE_TOKEN) using environment variables as shown in the config above.

Cursor

No explicit setup instructions for Cursor are provided in the repository.

Cline

No explicit setup instructions for Cline are provided in the repository.

Securing API Keys Example:

"env": {
  "BOOSTSPACE_API_BASE": "https://api.boost.space/v1",
  "BOOSTSPACE_TOKEN": "your-api-token"
}

Use environment variables to keep your API credentials secure.

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:

{
  "boostspace": {
    "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 “boostspace” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Does the server support Roots? Unknown (not documented)
Does the server support Sampling? Unknown (not documented)

Our opinion

The Boost.space MCP Server provides clear basic setup for Claude Desktop, but lacks documentation on MCP prompt templates, resources, and tools. It’s suitable as a reference for proxying an API to AI assistants, but would benefit from more comprehensive documentation and feature listings.

MCP Score