nexonco-mcp

nexonco-mcp is an advanced MCP server designed for accessing and analyzing clinical evidence data. It provides fast and flexible search capabilities across the CIViC (Clinical Interpretation of Variants in Cancer) database, supporting precision medicine and oncology research. Implemented in Python, it allows users to filter search results based on various parameters such as diseases, therapies, and genetic variants. This tool is particularly useful for researchers in the field of cancer, enabling them to analyze relevant data efficiently.

GitHub Stars

55

User Rating

Not Rated

Favorites

0

Views

124

Forks

8

Issues

1

Installation
Difficulty
Intermediate
Estimated Time
10-20 minutes
Requirements
Python 3.x
uvまたはDocker
+1 more

Installation

Installation

Prerequisites

Please specify required software and versions:
uv or Docker
Claude Desktop (for MCP integration)

Installation Steps

1. Clone Repository

bash
git clone https://github.com/Nexgene-Research/nexonco-mcp.git
cd nexonco-mcp

2. Install Dependencies

bash
pip install -r requirements.txt

3. Configure Claude Desktop

Edit claude_desktop_config.json to add the MCP server:
json
{
  "mcpServers": {
    "nexonco-mcp": {
      "command": "python",
      "args": ["server.py"]
    }
  }
}

4. Start Server

bash
python server.py

Troubleshooting

Common Issues

Issue: Server won't start Solution: Check Python version and reinstall dependencies. Issue: Not recognized by Claude Desktop Solution: Verify configuration file path and syntax.

Configuration

Configuration

Basic Configuration

Claude Desktop Setup

Edit ~/.config/claude-desktop/claude_desktop_config.json (macOS/Linux) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
json
{
  "mcpServers": {
    "nexonco-mcp": {
      "command": "python",
      "args": ["server.py"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Environment Variables

Set the following environment variables as needed:
bash
export API_KEY="your-api-key"
export DEBUG="true"

Advanced Configuration

Security Settings

Store API keys in environment variables or secure configuration files
Set appropriate file access permissions
Adjust logging levels

Performance Tuning

Configure timeout values
Limit concurrent executions
Set up caching

Configuration Examples

Basic Configuration

json
{
  "mcpServers": {
    "nexonco-mcp": {
      "command": "python",
      "args": ["server.py"],
      "env": {
        "PORT": "3000",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Advanced Configuration

json
{
  "mcpServers": {
    "advanced-mcp": {
      "command": "python",
      "args": ["-m", "server"],
      "cwd": "/path/to/server",
      "env": {
        "PYTHONPATH": "/path/to/modules",
        "CONFIG_FILE": "/path/to/config.json"
      }
    }
  }
}

Examples

Examples

Basic Usage

Here are basic usage examples for the MCP server:

Using with Claude Desktop

1Verify MCP Server Startup
Open Claude Desktop and confirm that the configuration has been loaded correctly.
2Execute Basic Commands

   Available tools from this MCP server:
   - search_clinical_evidence: A MCP tool for querying clinical evidence data that returns formatted reports.
   

Programmatic Usage

python

Python example

import requests def call_mcp_tool(tool_name, params): response = requests.post( 'http://localhost:3000/mcp/call', json={ 'tool': tool_name, 'parameters': params } ) return response.json()

Usage example

result = call_mcp_tool('search_clinical_evidence', { 'disease_name': 'Lung Non-small Cell Carcinoma', 'therapy_name': 'Cetuximab' }) print(result)

Advanced Examples

Automation Script

bash
#!/bin/bash

Batch processing example

for file in *.txt; do mcp-tool process "$file" done

API Integration

python

Python example

import requests import json def call_mcp_tool(tool_name, params): response = requests.post( 'http://localhost:3000/mcp/call', json={ 'tool': tool_name, 'parameters': params } ) return response.json()

Usage example

result = call_mcp_tool('search_clinical_evidence', { 'input': 'sample data', 'options': {'format': 'json'} })

Use Cases

Searching for information on genetic variants associated with specific cancers.
Collecting clinical evidence regarding the efficacy of new therapies.
Recommending appropriate treatments based on patient symptoms.
Analyzing research data to assist in writing scientific papers.

Additional Resources