SciPilot

Natural language interface for scientific command-line tools via Model Context Protocol (MCP).

SciPilot bridges the gap between natural language and scientific CLI tools. Define your tool once in YAML, then let LLMs handle the rest — parameters, file paths, output parsing, the works. Works for any command-line worth talking to.

Quick Start

git clone https://github.com/grebenyyk/scipilot
cd scipilot
pip install -e .
scipilot --tools-dir ./tools

Usage

1. Add tool descriptors to `tools/` (see `examples/raspa.yaml`)

2. Connect MCP client (Claude Desktop, VS Code, etc.)

3. Ask in natural language:

Tool Descriptor Format

You write a tool descriptor — a YAML file that tells SciPilot:

• What your tool expects (inputs, types, defaults)

• How to build the command (templates)

• Where to find the output (file paths, regex patterns)

SciPilot exposes these as MCP tools that any LLM can call.

tool:
  name: mytool
  binary: mytool
  
operations:
  - name: run_simulation
    description: "Run a simulation"
    inputs:
      - name: input_file
        type: file
        required: true
    outputs:
      - name: result
        path: "output.txt"
        extract_pattern: "Result: ([0-9.]+)"

See `examples/` for complete tool descriptors.

> ⚠️ Security Note: Tool YAML files execute with full shell privileges. Only load tool descriptors you trust and have reviewed. User inputs are substituted directly into shell command templates.

Project Structure

scipilot/
├── server.py          # MCP server entry point
├── tool_loader.py     # YAML parsing, tool discovery
├── executor.py        # Subprocess execution, output parsing
└── models.py          # Dataclasses for tool descriptors

tools/                 # Your tool descriptors (gitignored)
examples/              # Example descriptors

Development

# Run tests
pytest

# Type checking
mypy scipilot/

# Format
ruff format .

License

MIT