RDKit MCP Server: Agentic Access to RDKit for LLMs

RDKit MCP Server is an open-source MCP server that enables language models to interact with RDKit through natural language. The goal is to provide agent-level access to every function in RDKit 2025.3.1 without writing any code.

Features

• Seamless Integration: Exposes RDKit functions via the Model Context Protocol (MCP).
• Language Model Support: Connect any LLM that supports the MCP protocol.
• CLI Client: Includes a command-line client powered by OpenAI for quick experimentation.

Table of Contents

• Installation

• Usage

  • Start the Server
  • CLI Client

• Available Tools

• Evaluations

• Contributing

Installation

Install the package:

pip install .

Usage

Start the Server

python run_server.py [--settings settings.yaml]

See settings.example.yaml for setting options

Once the server is running, any MCP-compliant LLM can connect. For example, see the Claude Desktop quickstart.

CLI Client

A CLI client is included for rapid prototyping with OpenAI:

export OPENAI_API_KEY="sk-proj-xxx"
python run_client.py

Available Tools

List all available RDKit tools exposed by the server:

python list_tools.py [--settings settings.yaml]

Evaluations

The evals directory contains a test suite for evaluating RDKit MCP tool outputs and agent responses using pydantic-evals.

Install Dependencies

pip install ".[evals]"

Start the MCP Server

In one terminal, start the server:

python run_server.py

Run Evaluations

In another terminal, run the evaluation suite:

python evals/run_evals.py

Options:

• --verbose - Show detailed output including inputs and outputs
• --filter <name> - Run only cases matching the name
• --output-json results.json - Export results to JSON

Each test uses LLM-based evaluation (LLMJudge) to assess whether the agent correctly used the RDKit tools and produced accurate results.

Contributing

We welcome contributions, feature requests, and bug reports:

See CONTRIB.md for guidelines on how to get started.

Together, we can make RDKit accessible to a wider range of applications through natural language interfaces.