Lspace MCP Server

What does “Lspace” MCP Server do?

Lspace MCP Server is an open-source backend and standalone application that implements the Model Context Protocol (MCP). It is designed to eliminate context-switching friction for developers by enabling the capture of insights from any AI session, making them persistently available across various tools. By connecting AI agents and external tools to managed content repositories, Lspace turns scattered conversations into persistent, searchable knowledge. It enables workflows such as intelligent knowledge base generation, context enrichment for AI assistants, and seamless integration with tools that can query or update stored knowledge. Lspace empowers developers to integrate and manage knowledge repositories, facilitating enhanced development workflows and collaboration.

List of Prompts

No prompt templates could be identified from the provided files or documentation.

List of Resources

No explicit MCP “resources” are documented in the available files or README.

List of Tools

No explicit tool definitions (e.g., query_database, read_write_file, etc.) are documented or listed in the available files or documentation.

Use Cases of this MCP Server

• Knowledge Base Generation: Lspace enables capturing and storing insights and outputs from AI sessions, which can be managed as a persistent knowledge base.
• Contextual AI Assistance: Developers can use Lspace to enrich AI interactions with context from past conversations or repositories, improving accuracy and relevance.
• Repository Management: By configuring connections to local or GitHub repositories, Lspace helps manage code and documentation as context for AI agents.
• Seamless Tool Integration: Lspace makes insights available across multiple tools, reducing context-switching and improving workflow efficiency.

How to set it up

Windsurf

No platform-specific instructions for Windsurf found in the provided materials.

Claude

No platform-specific instructions for Claude found in the provided materials.

Cursor

1. Ensure prerequisites: Install Node.js (LTS), npm, and Git.
2. Clone the repository:

   git clone https://github.com/Lspace-io/lspace-server.git
   cd lspace-server
   

3. Install dependencies:

   npm install
   npm run build
   

4. Set up environment variables:

   cp .env.example .env
   # Edit .env to set OPENAI_API_KEY and other variables as needed
   

5. Configure repositories and credentials:

   cp config.example.json config.local.json
   # Edit config.local.json to add your GitHub PAT and repositories
   

6. In Cursor, configure your MCP server by adding this JSON snippet (replace the path with your actual path):

   {
     "mcpServers": [
       {
         "command": "node",
         "args": ["/actual/absolute/path/to/your/lspace-server/lspace-mcp-server.js"]
       }
     ]
   }
   

Securing API Keys

Store sensitive API keys (like OPENAI_API_KEY) in environment variables. Example configuration:

{
  "mcpServers": [
    {
      "command": "node",
      "args": ["/path/to/lspace-mcp-server.js"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key"
      },
      "inputs": {}
    }
  ]
}

Cline

No platform-specific instructions for Cline found in the provided materials.

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:

{
  "lspace-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can use this MCP as a tool with access to all its functions and capabilities. Remember to change “lspace-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the level of documentation, the presence of a clear overview, working setup, and some use case detail but lacking tool, prompt, resource, roots, and sampling documentation, I would rate this MCP server a 4/10 for completeness and developer experience.

MCP Score