Overview

Tool Agent is a Python package that converts any command-line tool into a Large Language Model (LLM) agent. This allows you to interact with the tool using natural language, making it easier to use and integrate with other applications.

How the Framework Works

Tool Agent leverages the Model Context Protocol (MCP) to bridge command-line tools and Large Language Models (LLMs). The framework works by converting CWL (Common Workflow Language) tool definitions into MCP-compatible agents that can be discovered and invoked by LLMs through natural language queries.

Here's how it works:

1. You create an MCP server instance using mcp_api
2. Register your domain-specific tools by providing their CWL definitions via add_tool()
3. Start the server
4. The MCP server exposes these tools as discoverable agents that any MCP-compatible client (like Cursor) can query and invoke

When an LLM needs to use a tool, it:

• Queries the MCP server for available tools
• Selects the appropriate one
• Invokes it with the necessary parameters
• The tool executes within a containerized environment (as specified in the CWL)
• Processes the request and returns results back through the MCP protocol to the LLM
• Presents the answer to the user in natural language

Advantages

• Local Data Handling: Run professional tools on local data securely, without uploading sensitive files to the cloud.
• Universal CMD Tool Integration: Easily convert any command-line tool into an MCP-based agent with minimal effort.
• CWL Ecosystem: Leverage the vast, open-source CWL community—especially strong in bioinformatics—for tool definitions and interoperability.
• Flexible Integration: Works with any LLM or client that supports MCP, enabling vendor-agnostic, modular workflows.
• User-Friendly: No need for users to install or configure complex tools; everything is managed by the agent and containers.
• No Prior Knowledge Required: Users can access advanced tools and workflows through natural language, lowering the barrier to entry.
• Reproducibility: CWL ensures that tool execution is standardized and reproducible across environments.
• Scalability: Easily add new tools or scale up by running multiple agents or servers.
• Security: Keep data within your infrastructure, reducing exposure to external threats.
• Extensibility: Add new tools, workflows, or integrations as your needs evolve.