1. 创建conda环境： ```bash conda create -n chemmcp python=3.11 conda activate chemmcp ```
If you already use ChemMCP Lite, the chemmcp lite mcp server is the piece that lets your assistant work with it directly. 1. 创建conda环境： bash conda create -n chemmcp python=3.11 conda activate chemmcp .
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
RDKit — 分子处理和计算的核心库PubChemPy — PubChem数据库接口SELFIES — 分子的自引用嵌入式字符串表示rdchiral — 手性分子处理synspace — 分子空间探索和修饰tavily-python — 网络搜索APIlitellm — 统一的LLM API接口requests — HTTP请求库transformers on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: TAVILY_API_KEY, LLM_MODEL_NAME, OPENAI_API_KEY, CHEMSPACE_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. ChemMCP Lite's toolset — RDKit, PubChemPy, SELFIES and 5 more — is a fair guide to whether it matches your workflow. It is maintained by cassiusoat; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| RDKit | 分子处理和计算的核心库 |
| PubChemPy | PubChem数据库接口 |
| SELFIES | 分子的自引用嵌入式字符串表示 |
| rdchiral | 手性分子处理 |
| synspace | 分子空间探索和修饰 |
| tavily-python | 网络搜索API |
| litellm | 统一的LLM API接口 |
| requests | HTTP请求库 |
{
"mcpServers": {
"ChemMCP": {
"command": "/opt/miniconda3/envs/chemmcp/bin/python", // 如何找到路径: conda activate chemmcp && which python
"args": [
"-m", "chemmcp",
"--tools",
"WebSearch",
"MoleculeWeight",
"MoleculeAtomCount",
"MoleculeSimilarity",
"FunctionalGroups",
"SmilesCanonicalization",
"Iupac2Smiles",
"Smiles2Iupac",
"Smiles2Formula",
"Name2Smiles",
"Selfies2Smiles",
"Smiles2Selfies",
"Smiles2Cas",
"MoleculeSmilesCheck",
"ReactionSmilesCheck",
"MoleculeVisualizer",
"MoleculeCaptioner",
"MoleculeGenerator",
"MoleculeModifier"
],
"toolCallTimeoutMillis": 300000,
"env": {
"TAVILY_API_KEY": "your_tavily_api_key_here",
"LLM_MODEL_NAME": "openai/gpt-4o",
"OPENAI_API_KEY": "your_openai_api_key_here",
"CHEMSPACE_API_KEY": "your_chemspace_api_key_here", // 可选：仅在需要额外数据库查找时使用
"PATH": "/opt/miniconda3/envs/chemmcp/bin:/usr/local/bin:/usr/bin:/bin" // 如何找到路径: conda activate chemmcp && echo $PATH
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| TAVILY_API_KEY | Credential the server authenticates with. | Yes |
| LLM_MODEL_NAME | Configuration value read at startup. | Optional |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| CHEMSPACE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.