OpenReview Python MCP Server

A Model Context Protocol (MCP) server built with FastMCP that exposes the structure and documentation of the openreview-py library. This allows LLM clients to discover available classes, functions, and their documentation to generate accurate Python code examples using the OpenReview library.

🎯 Purpose

This MCP server provides read-only access to the openreview-py library's structure - no code execution or API calls are performed. It's designed to help LLMs understand and generate code using the OpenReview Python library by providing:

• Function signatures and documentation

• Class structures and methods

• Search capabilities across the library

• Comprehensive library overview

🚀 Quick Start

Prerequisites

• Python 3.11+

• uv package manager

Installation

1. Clone and navigate to the project:

cd openreview-mcp

2. Install dependencies:

uv sync

This will install:

• fastmcp - MCP server framework

• openreview-py - The library we're introspecting (from GitHub)

• Development tools (pytest, black, ruff, mypy)

Running the Server

Start the MCP server:

uv run openreview-mcp

Or run directly:

uv run python src/server.py

The server will start and display available tools:

Starting OpenReview Python Library MCP Server...
Available tools:
- list_openreview_functions: List all available functions
- list_openreview_classes: List all available classes
- search_openreview_api: Search functions by keyword
- get_openreview_overview: Get library overview
- get_function_details: Get detailed function information

🛠️ Available MCP Tools

1. list_openreview_functions

Lists all available functions from the openreview-py library.

Parameters:

• filter_by_module (optional): Filter by specific module (e.g., "openreview.api")

Returns: Array of functions with name, docstring, module, signature, and type.

2. list_openreview_classes

Lists all available classes from the openreview-py library.

Parameters:

• include_methods (default: true): Whether to include class methods

Returns: Array of classes with name, docstring, module, and methods.

3. search_openreview_api

Search for functions by name or keywords in docstrings.

Parameters:

• query (required): Search term

Returns: Array of matching functions.

4. get_openreview_overview

Get a comprehensive overview of the entire library.

Returns: Dictionary with functions, classes, modules, and statistics.

5. get_function_details

Get detailed information about a specific function.

Parameters:

• function_name (required): Name of the function

Returns: Detailed function information.

📁 Project Structure

openreview-mcp/
├── src/
│   ├── server.py          # FastMCP server implementation
│   ├── introspect.py      # Library introspection utilities
│   └── __init__.py        # (optional) package init
├── pyproject.toml         # Project configuration and dependencies
└── README.md              # This file

🔧 Development

Current Implementation Status

✅ Implemented:

• Complete FastMCP server setup with 5 tools

• Project structure and configuration

• Stub implementations with realistic example data

🚧 TODO - Future Enhancements:

The current implementation uses stub data. Here's how to expand it:

1. Real Library Introspection (introspect.py)

# TODO: Replace stub implementations with real introspection
import openreview
import inspect

def get_openreview_functions():
    # Use inspect module to dynamically discover functions
    # Walk through openreview module and submodules
    # Extract real docstrings, signatures, parameters
    pass

2. Enhanced Search (server.py)

# TODO: Implement advanced search features
- Fuzzy string matching
- Search in parameter names and types  
- Regex pattern support
- Result ranking by relevance

3. Caching and Performance

# TODO: Add caching for introspection results
- Cache function/class discoveries
- Lazy loading of module information
- Performance monitoring and optimization

4. Advanced Filtering

# TODO: Add sophisticated filtering options
- Filter by function complexity
- Filter by parameter count
- Include/exclude private methods
- Filter by inheritance hierarchy

Development Commands

# Install development dependencies
uv sync --dev

# Run tests (when implemented)
uv run pytest

# Format code
uv run black .

# Lint code
uv run ruff check .

# Type checking
uv run mypy .

Adding New MCP Tools

To add a new tool to the server:

1. Define the tool function in server.py:

@mcp.tool()
def your_new_tool(param1: str, param2: int = 10) -> Dict[str, Any]:
    """
    Description of what your tool does.
    
    Args:
        param1: Description of parameter
        param2: Optional parameter with default
        
    Returns:
        Description of return value
    """
    # Implementation here
    return {"result": "your data"}

2. Add any supporting logic to introspect.py if needed:

def supporting_function():
    """Helper function for your new tool."""
    pass

3. Update the main() function to advertise the new tool:

def main():
    print("Available tools:")
    print("- your_new_tool: Description")

🔍 Usage Examples

With Claude Desktop

Configure the server in your Claude Desktop MCP settings:

{
  "mcpServers": {
    "openreview": {
      "command": "uv",
      "args": ["run", "python", "/path/to/openreview-mcp/src/server.py"],
      "host": "localhost",
      "port": 4000
    }
  }
}

With VS Code

You can also configure the MCP server in VS Code using the Model Context Protocol extension (or compatible MCP client):

1. Open the command palette and search for "MCP: Add Server" or open the MCP extension settings.

2. Make sure the port matches your deployment (default is 4000, or set MCP_PORT env variable).

3. Save and connect to the server from the MCP extension sidebar.

Example Tool Usage

Once connected (in Claude Desktop or VS Code), you can ask:

"What functions are available for working with notes in OpenReview?"

The client would use the search_openreview_api tool with query "note" to find relevant functions.

"Show me all the classes in the openreview library"

The client would use list_openreview_classes to get the class information.

🤝 Contributing

Key areas for contribution:

1. Implement real introspection in introspect.py using Python's inspect module

2. Add comprehensive error handling for import failures and edge cases

3. Implement caching for better performance

4. Add tests for all functionality

5. Enhance search capabilities with fuzzy matching and ranking

⚠️ Important Notes

• Read-only: This server only provides metadata - no code execution

• No API calls: No actual OpenReview API interactions

• No authentication: No API keys or credentials required

• Educational: Designed for code generation assistance, not production API usage

📄 License

This project is provided as-is for educational and development purposes.