rdkit-mcp-server
MCP server that enables language models to interact with RDKit through natural language
Install / Use
claude mcp add tandemai-inc -- npx -y github:tandemai-inc/rdkit-mcp-serverIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Tags
Our assessment of rdkit-mcp-server
rdkit-mcp-server scores 69/100 on our quality scale, 897th of 963 AI & Machine Learning skills we index.
Its MCP Server is 2.4 KB long, well organised into 13 sections with 7 code examples: moderately detailed.
It has 48 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- Our last check on 2026-09-21 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 86/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-29. Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
rdkit-mcp-server compared with similar skills
All 4 of these similar skills score higher than rdkit-mcp-server; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| rdkit-mcp-server (this skill)by tandemai-inc | 69 | 48 | 6mo ago | MCP Server |
| claude-memby thedotmack | 100 | 113.6k | today | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.3k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 91.0k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 79.0k | today | CLAUDE.md |
Frequently asked questions
- How do I install rdkit-mcp-server?
- Run
claude mcp add tandemai-inc -- npx -y github:tandemai-inc/rdkit-mcp-server. The install tabs above show the steps for each supported agent. - Which AI agents does rdkit-mcp-server work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is rdkit-mcp-server safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 86/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is rdkit-mcp-server still maintained?
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubRDKit 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
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.
Related Skills
claude-mem
113.6kPersistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
Agent-Reach
109.3kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Understand-Anything
91.0kGraphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
headroom
79.0kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.