OrigeneMCP

Overview

1. OrigeneMCP Overview

OrigeneMCP is the MCP toolkit for the 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.

2. Deploy OrigeneMCP

Clone the repository:

git clone https://github.com/GENTEL-lab/OrigeneMCP.git

The 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:

pip install uv

uv venv .venv --python=3.13

source .venv/bin/activate

Initialize the dependencies of this repository:

uv sync

Deploy the mcp service:

cp default.conf.toml local.conf.toml
# get your tavily_api_key in https://tavily.com/
# get your jina_api_key in https://jina.ai/

export PYTHONPATH=`fab pypath`
uv run -m deploy.web

NOTE:

Please ensure that port 8788 is not occupied. If it is occupied, please modify the port in file local.conf.toml to another port;
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;

3. Use OrigeneMCP

First, connect to OrigeneMCP and get all available tools.

from langchain_mcp_adapters.client import MultiServerMCPClient

# Choose the packages you want to use
tool_packages = [
    "chembl", "kegg", "string", "search", "pubchem", "ncbi",
    "uniprot", "tcga", "ensembl", "ucsc", "fda_drug", "pdb",
    "monarch", "clinicaltrials", "dbsearch", "opentargets"
]

mcp_servers = {
    package: {
        "transport": "streamable_http",
        "url": f"http://127.0.0.1:8788/{package}/mcp/"
    } for package in tool_packages
}

client = MultiServerMCPClient(mcp_servers)
tools = await client.get_tools()
print(f"✅ Found {len(tools)} mcp tools")
tool_map = {tool.name: tool for tool in tools}

Let'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:

tool_name = "get_general_info_by_protein_or_gene_name"
tool = tool_map[tool_name]
print(tool.description)
# Get general information of a protein or gene by name from UniProt database.
# Args:
#     name: Protein or gene name.
#     sepcies: Species name.
# ...

Set input parameters and call the tool:

query_args = {"query": "TP53"}
result = await tool.ainvoke(query_args)
print(result)
# {
#     "genes": [
#     {
#         "geneName": {
#         "evidences": [
#             {
#             "evidenceCode": "ECO:0000312",
# ...
# }

4. Use OrigeneMCP through CAMEL

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.

Install the CAMEL package:

pip install camel-ai

Set up your OpenAI API key:

export OPENAI_API_KEY='your_openai_api_key'

Use the CAMEL interface to invoke the OrigeneMCP-based Agent:

import asyncio

from camel.agents import ChatAgent
from camel.toolkits import OrigeneToolkit

# Choose the packages you want to use
tool_packages = [
    "chembl", "kegg", "string", "search", "pubchem", "ncbi",
    "uniprot", "tcga", "ensembl", "ucsc", "fda_drug", "pdb",
    "monarch", "clinicaltrials", "dbsearch", "opentargets"
]

config = {
    "mcpServers": {
        package: {
            "transport": "streamable_http",
            "url": f"http://127.0.0.1:8788/{package}/mcp/"
        } for package in tool_packages
    }
}

async def main():
    # Use async context manager for automatic connection management
    async with OrigeneToolkit(config_dict=config) as origene_toolkit:
        user_msg = "what can you do?"
        agent = ChatAgent(
            "You are named origene assistant.",
            model="gpt-4o",
            tools=[*origene_toolkit.get_tools()],
        )
        response = agent.step(user_msg)
        print(response.msgs[0].content)

asyncio.run(main())

4. Cite OrigeneMCP

Any publication that discloses findings arising from using this source code should cite:

@article{origene,
    title={{OriGene}: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery},
    author={Zhang, Zhongyue and Qiu, Zijie and Wu, Yingcheng and Li, Shuya and Wang, Dingyan and Zhou, Zhuomin and An, Duo and Chen, Yuhan and Li, Yu and Wang, Yongbo and Ou, Chubin and Wang, Zichen and Chen, Jack Xiaoyu and Zhang, Bo and Hu, Yusong and Zhang, Wenxin and Wei, Zhijian and Ma, Runze and Liu, Qingwu and Dong, Bo and He, Yuexi and Feng, Qiantai and Bai, Lei and Gao, Qiang and Sun, Siqi and Zheng, Shuangjia},
    journal={bioRxiv},
    year={2025},
    publisher={Cold Spring Harbor Laboratory}
}

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An advanced integrated MCP server platform that combines 600+ tools and multiple biomedical databases to enable comprehensive information retrieval across molecules, proteins, genes, and diseases for accelerating therapeutic research.

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OrigeneMCP

OrigeneMCP

1. OrigeneMCP Overview

2. Deploy OrigeneMCP

3. Use OrigeneMCP

4. Use OrigeneMCP through CAMEL

4. Cite OrigeneMCP

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