Quick Start

Get up and running with Tool Agent in minutes.

Basic Setup

The framework allows you to set up an MCP server with predefined tools for specific domains. For example, to create a bioinformatics-focused MCP server:

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

Start MCP Server

python examples/bioinfo_question.py

Configure MCP Client

Once the server is running, you can configure your MCP client (e.g., in Cursor) to connect to it:

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

Start Using

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.

Next Steps

• Learn more about MCP Server setup
• Explore Function Call integration
• Check out use cases