biocontext 0.0.8

Python package for use with

BioContext

Python package for use with BioContext MCP servers to enable LLM tool use.

• Github repository: https://github.com/biocypher/biocontext/
• Documentation https://biocypher.github.io/biocontext/

Installation

pip install biocontext

Quick Start

from biocontext import MCPConnection, OpenAPIServerFactory

# Connect to an existing MCP server
async with MCPConnection("my-server") as conn:
    # Get available tools
    tools = await conn.get_tools()

    # Call a tool
    result = await conn.call_tool("tool-name", {"param": "value"})

# Create MCP servers from OpenAPI specs
factory = OpenAPIServerFactory()
servers = await factory.create_servers()

Use Cases

1. Connecting to Existing MCP Servers

Use MCPConnection to connect to any MCP server and access its tools:

from biocontext import MCPConnection

async def use_remote_tools():
    async with MCPConnection("my-server") as conn:
        # List available tools
        tools = await conn.get_tools()
        print(f"Available tools: {list(tools.keys())}")

        # Call a tool
        result = await conn.call_tool("search", {"query": "example"})
        print(f"Search results: {result}")

2. Creating MCP Servers from OpenAPI Specs

Use OpenAPIServerFactory to create MCP servers from OpenAPI specifications:

from biocontext import OpenAPIServerFactory

async def create_servers():
    # Create servers from OpenAPI specs
    factory = OpenAPIServerFactory()
    servers = await factory.create_servers()

    # Use the created servers
    for server in servers:
        tools = await server.get_tools()
        print(f"Server {server.name} has tools: {list(tools.keys())}")

Configuration

OpenAPI Server Configuration

Create a config.yaml file in the biocontext/config directory:

schemas:
  - name: example-server
    url: https://api.example.com/openapi.json
    type: json
    base: https://api.example.com

Integration with LLMs

The package is designed to work seamlessly with LLMs. Here's a typical workflow:

1. Connect to or create MCP servers
2. Get available tools and their descriptions
3. Use the tools in your LLM prompts
4. Call tools based on LLM decisions

Example with an LLM:

from biocontext import MCPConnection

async def llm_workflow():
    async with MCPConnection("my-server") as conn:
        # Get tool descriptions for the LLM
        tools = await conn.get_tools()

        # Example LLM prompt
        prompt = f"""
        Available tools:
        {tools}

        User request: Search for information about proteins
        """

        # LLM decides to use the search tool
        result = await conn.call_tool("search", {"query": "proteins"})

Installation

pip install biocontext

Requirements

• Python 3.8+
• fastmcp
• httpx
• requests
• pyyaml

Features

• Simple interface for interacting with MCP servers
• Support for OpenAPI-based MCP servers
• Async/await support for efficient I/O operations
• Utility functions for common operations

Development

We use uv for dependency management and versioning. The script bump.sh is used to bump the version and create a git tag, which then can be used to create a release on GitHub that triggers publication to PyPI. It can be used with semantic versioning by passing the bump type as an argument, e.g. ./bump.sh patch.

License

MIT License - see LICENSE file for details