MCP Server

The framework allows you to set up an MCP server with predefined tools for specific domains.

Basic Example

For example, to create a bioinformatics-focused MCP server, you can use the following setup (as shown in examples/bioinfo_question.py):

from coala.mcp_api import mcp_api

mcp = mcp_api(host='0.0.0.0', port=8000)
mcp.add_tool('examples/ncbi_datasets_gene.cwl', 'ncbi_datasets_gene')
mcp.add_tool('examples/bcftools_view.cwl', 'bcftools_view', read_outs=False)
mcp.serve()

This creates an MCP server that exposes two bioinformatics tools:

• ncbi_datasets_gene: Retrieves gene metadata from NCBI datasets
• bcftools_view: Subsets and filters VCF/BCF files

MCP Client Configuration

You can configure your MCP client (e.g., in Cursor) to connect to the server in two ways:

Option 1: Using Command Directly (Recommended)

The configuration can use a command directly, which will automatically start the server:

{
    "mcpServers": {
        "pdf": {
            "command": "python",
            "args": ["/path/to/bioinfo_question.py"]
        }
    }
}

Option 2: Using URL (Server Must Be Running)

Alternatively, starting the Server

python examples/bioinfo_question.py

If the server is already running, you can connect via URL:

{
    "mcpServers": {
        "coala": {
            "url": "http://localhost:8000/mcp",
            "transport": "streamable-http"
        }
    }
}

Usage Examples

With this setup, you can ask the LLM natural language questions like:

• "Give me a summary about gene BRCA1"
• "Subset variants in the gene BRCA1 from the https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz"

The LLM will automatically discover the available tools, understand their parameters, invoke the appropriate tool with the correct arguments, and present the results in a user-friendly format.

Demo