What is Agensight Mcpserver

agensight_mcpserver is a tool designed to automatically extract agent information from codebases, facilitating the analysis and configuration of AI agents within software projects.

Use cases

Use cases include analyzing codebases for AI agent configurations, generating prompt templates for machine learning models, and visualizing interactions between different agents in a project.

How to use

To use agensight_mcpserver, clone the repository, set up a virtual environment, install dependencies, and configure it in your Cursor settings. Then, invoke the tool to analyze your codebase and generate a structured configuration file.

Key features

Key features include agent discovery, prompt extraction, tool identification, connection mapping, and JSON output for easy integration.

Where to use

agensight_mcpserver is applicable in software development, particularly in projects involving AI agents and machine learning models, where understanding agent interactions is crucial.

AgentSight MCP Server

A tool to automatically extract agent information from codebases.

Quick Setup

# Clone repository
git clone [email protected]:PYPE-AI-MAIN/agensight_mcpserver.git
cd agensight_mcpserver

# Create virtual environment
python -m venv mcp-env
source mcp-env/bin/activate  # On Windows: mcp-env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Use with Cursor

1. Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "agensight-server": {
      "command": "/path/to/agensight_mcpserver/mcp-env/bin/python",
      "args": [
        "/path/to/agensight_mcpserver/server.py"
      ],
      "description": "tool to generate agensight config"
    }
  }
}

Note: Replace /path/to/agensight_mcp_server with your actual installation path. The virtual environment’s Python is located at mcp-env/bin/python relative to your installation directory.

2. In Cursor, ask:

Please analyze this codebase using the generateAgensightConfig MCP tool

3. Get a agensight.config.json file with all agents, prompts, tools, and connections.

Features

• Agent Discovery: Automatically identifies AI agents in your codebase
• Prompt Extraction: Locates and extracts the full text of prompt templates
• Tool Identification: Maps all tools used by each agent
• Connection Mapping: Visualizes how agents interact with each other
• JSON Output: Generates a structured configuration file for easy integration

How It Works

The server uses the Machine Conversation Protocol to provide a tool that analyzes your codebase. When invoked, it:

1. Recursively scans all files in your project
2. Identifies classes and functions that interact with LLMs
3. Extracts complete prompt templates by following variable references
4. Maps the connections between agents
5. Outputs a structured JSON configuration

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.