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\n | Product |\n Paper |\n OriGene Code |\n OrigeneMCP Code |\n

\n\n## 1. OrigeneMCP Overview\n> Recent updates:\n> - **2025-07-28** 🚀 OriGene officially launched at WAIC 2025!\n> - **2025-07-22** 🔥 Integrated OrigeneMCP into the Bohrium AI for Science platform.\n> - **2025-07-19** 🔥 Integrated OrigeneMCP into CAMEL.\n> - **2025-07-18** 🚀 OrigeneMCP officially open-sourced!\n\n\n![Overview](./assets/OrigeneMCP-overview.jpg)\n\nOrigeneMCP is the MCP toolkit for the [OriGene](https://github.com/GENTEL-lab/OriGene) project that integrates over **600** tools and multiple databases (including ChEMBL, PubChem, FDA, OpenTargets, NCBI, UniProt, PDB, Ensembl, UCSC, KEGG, STRING, TCGA, Monarch, ClinicalTrials, and more) into an advanced integrated MCP server platform, combining disease biology and drug discovery tools to enable comprehensive multi-dimensional information retrieval across small molecules, proteins, genes, diseases, and other biological entities, serving as a unified interface for accessing and analyzing complex biomedical data to accelerate therapeutic research and development.\n\n## 2. Deploy OrigeneMCP\nClone the repository:\n\n```bash\ngit clone https://github.com/GENTEL-lab/OrigeneMCP.git\n```\n\nThe dependencies of this repository are managed by uv. Before deployment, please make sure that uv is already installed. Use the following commands to install uv:\n\n\n```bash\npip install uv\n```\n\n```bash\nuv venv .venv --python=3.13\n```\n\n```bash\nsource .venv/bin/activate\n```\n\nInitialize the dependencies of this repository:\n```bash\nuv sync\n```\n\nDeploy the mcp service:\n```bash\ncp default.conf.toml local.conf.toml\n# get your tavily_api_key in https://tavily.com/\n# get your jina_api_key in https://jina.ai/\n\nexport PYTHONPATH=`fab pypath`\nuv run -m deploy.web\n```\n\n**NOTE:** \n* Please ensure that port 8788 is not occupied. If it is occupied, please modify the port in file `local.conf.toml` to another port;\n* If you need to use Tavily search or Jina search, please configure the `tavily_api_key` or `jina_api_key` in file `local.conf.toml`;\n\n## 3. Use OrigeneMCP\n### 3.1. Use locally deployed OrigeneMCP\nFirst, connect to OrigeneMCP and get all available tools.\n\n```python\nfrom langchain_mcp_adapters.client import MultiServerMCPClient\n\n# Choose the packages you want to use\ntool_packages = [\n \"chembl\", \"kegg\", \"string\", \"search\", \"pubchem\", \"ncbi\",\n \"uniprot\", \"tcga\", \"ensembl\", \"ucsc\", \"fda_drug\", \"pdb\",\n \"monarch\", \"clinicaltrials\", \"dbsearch\", \"opentargets\"\n]\ntool_packages = [server+'_mcp' for server in tool_packages]\n\nmcp_servers = {\n package: {\n \"transport\": \"streamable_http\",\n \"url\": f\"http://127.0.0.1:8788/{package}/mcp/\"\n } for package in tool_packages\n}\n\nclient = MultiServerMCPClient(mcp_servers)\ntools = await client.get_tools()\nprint(f\"✅ Found {len(tools)} mcp tools\")\ntool_map = {tool.name: tool for tool in tools}\n```\n\nLet's explore a specific tool's capabilities by examining its description and test cases. As an example, we'll look at the `get_general_info_by_protein_or_gene_name` tool from UniProt, which provides comprehensive protein and gene information:\n```python\ntool_name = \"get_general_info_by_protein_or_gene_name\"\ntool = tool_map[tool_name]\nprint(tool.description)\n# Get general information of a protein or gene by name from UniProt database.\n# Args:\n# name: Protein or gene name.\n# sepcies: Species name.\n# ...\n\n```\n\nSet input parameters and call the tool:\n```python\nquery_args = {\"query\": \"TP53\"}\nresult = await tool.ainvoke(query_args)\nprint(result)\n# {\n# \"genes\": [\n# {\n# \"geneName\": {\n# \"evidences\": [\n# {\n# \"evidenceCode\": \"ECO:0000312\",\n# ...\n# }\n\n```\n\n\n## 3.2. Use OrigeneMCP through CAMEL\n[CAMEL](https://github.com/camel-ai/camel) is an open-source community dedicated to finding the scaling laws of agents. In addition to local invocation, we can also use OrigeneMCP-based Agents through the CAMEL interface.\n\n1. **Install the CAMEL package:**\n```bash\npip install camel-ai\n```\n\n2. **Set up your OpenAI API key:**\n```bash\nexport OPENAI_API_KEY='your_openai_api_key'\n```\n\n3. **Use the CAMEL interface to invoke the OrigeneMCP-based Agent:**\n```python\nimport asyncio\n\nfrom camel.agents import ChatAgent\nfrom camel.toolkits import OrigeneToolkit\n\n# Choose the packages you want to use\ntool_packages = [\n \"chembl\", \"kegg\", \"string\", \"search\", \"pubchem\", \"ncbi\",\n \"uniprot\", \"tcga\", \"ensembl\", \"ucsc\", \"fda_drug\", \"pdb\",\n \"monarch\", \"clinicaltrials\", \"dbsearch\", \"opentargets\"\n]\ntool_packages = [server+'_mcp' for server in tool_packages]\n\nconfig = {\n \"mcpServers\": {\n package: {\n \"transport\": \"streamable_http\",\n \"url\": f\"http://127.0.0.1:8788/{package}/mcp/\"\n } for package in tool_packages\n }\n}\n\nasync def main():\n # Use async context manager for automatic connection management\n async with OrigeneToolkit(config_dict=config) as origene_toolkit:\n user_msg = \"what can you do?\"\n agent = ChatAgent(\n \"You are named origene assistant.\",\n model=\"gpt-4o\",\n tools=[*origene_toolkit.get_tools()],\n )\n response = agent.step(user_msg)\n print(response.msgs[0].content)\n\nasyncio.run(main())\n\n```\n\n## 3.3. Use OrigeneMCP through Bohrium\nBohrium AI for Science platform is dedicated to providing a new research paradigm for scientists worldwide in the AI era, empowering scientific exploration through technological innovation and making the acquisition, understanding, and application of knowledge more intelligent and efficient. OrigeneMCP can be directly accessed and invoked through the [Bohrium AI for Science platform](https://www.bohrium.com/apps/origene).\n\n\n\n## 4. 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