run

a.k.a. runtool: the bridge between human and AI tooling

Define functions in a Runfile. Your AI agent discovers and executes them via the built-in MCP server. You run them from the terminal too with instant startup and tab completion. Shell, Python, Node—whatever fits the task.

# Runfile

# @desc Search the codebase for a pattern
# @shell python
# @arg pattern The regex pattern to search for
search(pattern: str) {
    import sys, os, re
    for root, _, files in os.walk('.'):
        for f in files:
            path = os.path.join(root, f)
            try:
                for i, line in enumerate(open(path), 1):
                    if re.search(sys.argv[1], line):
                        print(f"{path}:{i}: {line.rstrip()}")
            except: pass
}

# @desc Deploy to an environment
deploy(env: str, version = "latest") {
    echo "Deploying $version to $env..."
    ./scripts/deploy.sh $env $version
}

# @desc Analyze a JSON file
function analyze(file: str) {
    #!/usr/bin/env python3
    import sys, json
    with open(sys.argv[1]) as f:
        data = json.load(f)
        print(f"Found {len(data)} records")
}

The syntax is designed to be similar to bash/sh, while being permissive & flexible, with added features for AI integration.

Humans can run these functions directly from the terminal:

$ run search "TODO"
$ run deploy staging
$ run analyze data.json

Point your AI agent at the Runfile, and it can discover and execute these tools automatically.

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Table of Contents

• AI Agent Integration (MCP)
• Installation
• The Runfile Syntax
  • Basic Functions
  • Block Syntax
  • Function Signatures
  • Attributes & Polyglot Scripts
  • Nested Namespaces
  • Function Composition
• Configuration
• License

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AI Agent Integration (MCP)

run has built-in support for the Model Context Protocol (MCP), allowing AI agents like Claude to discover and execute your Runfile functions as tools.

MCP Server Mode

Configure in your AI client (e.g., Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "my-project": {
      "command": "run",
      "args": ["--serve-mcp", "--runfile", "/path/to/your/project/"]
    }
  }
}

Now your AI agent can discover and call your tools automatically.

For debugging purposes, you can start run as an MCP server yourself:

run --serve-mcp

Describing Tools for AI

Use @desc to describe what a function does, and declare parameters in the function signature:

# @desc Search the codebase for a regex pattern
search(pattern: str) {
    #!/usr/bin/env python3
    import sys, os, re
    for root, _, files in os.walk('.'):
        for f in files:
            path = os.path.join(root, f)
            try:
                for i, line in enumerate(open(path), 1):
                    if re.search(sys.argv[1], line):
                        print(f"{path}:{i}: {line.rstrip()}")
            except: pass
}

# @desc Deploy the application to a specific environment
deploy(environment: str, version = "latest") {
    ./scripts/deploy.sh $environment $version
}

Functions with @desc are automatically exposed as MCP tools with typed parameters.

Inspect Tool Schema

View the generated JSON schema for all MCP-enabled functions:

run --inspect

This outputs the tool definitions that AI agents will see—useful for debugging and validation.

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Installation

Recommended

macOS/Linux (Homebrew)

brew tap nihilok/tap
brew install runtool

Windows (Scoop)

scoop bucket add nihilok https://github.com/nihilok/scoop-bucket
scoop install runtool

Alternative: Cargo

Works on all platforms:

cargo install run  # or: cargo install runtool

Tab Completions

Auto-detect your shell and install completions:

run --install-completion

Supports bash, zsh, fish, and powershell.

---

The Runfile Syntax

run parses your Runfile to find function definitions. The syntax is designed to be familiar to anyone who has used bash or sh.

Basic Functions

For simple, one-line commands, you don't need braces.

# Usage: run dev
dev() cargo run

# Usage: run fmt
fmt() cargo fmt

Block Syntax

Use {} for multi-statement functions. This avoids the need for trailing backslashes.

ci() {
    echo "Running CI..."
    cargo fmt -- --check
    cargo clippy
    cargo test
    echo "Done!"
}

Function Signatures

Declare parameters directly in the function signature for cleaner, self-documenting code:

# @desc Deploy to an environment
deploy(env: str, version = "latest") {
    echo "Deploying $version to $env..."
    ./scripts/deploy.sh $env $version
}

# @desc Resize an image
resize(width: int, height: int, file: str) {
    convert $file -resize ${width}x${height} output.png
}

Type annotations (str, int, bool) are used for:

• MCP JSON schema generation (AI agents see typed parameters)
• Self-documenting functions
• Optional runtime validation

Default values make parameters optional:

# version defaults to "latest" if not provided
deploy(env: str, version = "latest") { ... }

Legacy positional syntax still works for simple cases:

# Access arguments as $1, $2, $@
deploy() {
    env=$1
    version=${2:-latest}
    ./scripts/deploy.sh $env $version
}

Combining with @arg for descriptions:

# @desc Deploy the application
# @arg env Target environment (staging|prod)
# @arg version Version tag to deploy
deploy(env: str, version = "latest") {
    ./scripts/deploy.sh $env $version
}

When both signature params and @arg exist, the signature defines names/types/defaults, and @arg provides descriptions for MCP.

Attributes & Polyglot Scripts

You can use comment attributes (# @key value) or shebang lines to modify function behaviour and select interpreters.

Platform Guards (@os)

Restrict functions to specific operating systems. This allows you to define platform-specific implementations of the same task.

# @os windows
clean() del /Q dist

# @os unix
clean() rm -rf dist

When you run run clean, only the variant matching your current OS will execute.

Interpreter Selection

There are two ways to specify a custom interpreter:

1. Shebang detection

The first line of your function body can be a shebang, just like standalone scripts:

analyze() {
    #!/usr/bin/env python
    import sys, json
    with open(sys.argv[1]) as f:
        data = json.load(f)
        print(f"Found {len(data)} records")
}

server() {
    #!/usr/bin/env node
    const port = process.argv[1] || 3000;
    require('http').createServer((req, res) => {
        res.end('Hello!');
    }).listen(port);
}

2. Attribute syntax (@shell):

Use comment attributes for explicit control or when you need to override a shebang:

# @shell python3
calc() {
    import sys, math
    radius = float(sys.argv[1])
    print(f"Area: {math.pi * radius**2:.2f}")
}

Precedence: If both are present, @shell takes precedence over the shebang.

Supported interpreters: python, python3, node, ruby, pwsh, bash, sh

Nested Namespaces

Organise related commands using colons. run parses name:subname as a single identifier.

docker:build() docker build -t app .
docker:up() docker compose up -d
docker:logs() docker compose logs -f

Execute them with spaces:

$ run docker build
$ run docker logs

Function Composition

Functions can call other functions defined in the same Runfile, enabling task composition and code reuse without duplication.

# Base tasks
build() cargo build --release
test() cargo test
lint() cargo clippy

# Composed task that calls other functions
ci() {
    echo "Running CI pipeline..."
    lint
    test
    build
}

# Deploy depends on successful build
deploy() {
    build || exit 1
    echo "Deploying..."
    scp target/release/app server:/bin/
}

When you run run ci, all compatible functions are automatically injected into the execution scope, so you can call them directly without spawning new processes.

Key features:

• Functions can call sibling functions defined in the same file
• Exit codes are properly propagated (use || exit 1 to stop on failure)
• Works across different shells when interpreters are compatible
• Top-level variables are also available to all functions

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Configuration

Shell Selection

By default, run uses:

• Windows: PowerShell (pwsh if available, else powershell)
• Unix: sh

You can override this default by setting the RUN_SHELL environment variable.

# Force Zsh for this command
RUN_SHELL=zsh run build

# Make it permanent for your session
export RUN_SHELL=bash

Note: The commands in your Runfile must be compatible with the configured shell, unless an explicit interpreter (e.g., # @shell python) is defined for that function.

Global Runfile

Create a ~/.runfile in your home directory to define global commands available anywhere.

# ~/.runfile

# Usage: run update
update() {
    brew update
    brew upgrade
    rustup update
}

# Usage: run clone <repo>
clone() {
    git clone "https://github.com/$1"
    cd "$(basename "$1" .git)"
}

If a local ./Runfile exists, run looks there first. If the command isn't found locally, it falls back to ~/.runfile.

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License

MIT