Tilt MCP Server

A Model Context Protocol (MCP) server that integrates with Tilt to provide programmatic access to Tilt resources and logs through LLM applications.

Why use a Tilt MCP server?

Imagine prompting like this:

> Please work on {some LLM request} and then check tilt MCP for "backend-api" resource logs for compile status. Make sure that "backend-tests" resource is successful with your changes.

The key insight is you no longer need to tell your LLM _how_ to build and deploy your code. Instead, you can simply ask it to _what_ to build and deploy.

Tilt is a powerful tool for working with Docker/Kubernetes workloads. With the Tilt MCP server, you can integrate Tilt's features directly into your workflow using Large Language Models (LLMs) like Claude Code / Codex / Gemini / VS Code Copilot / etc.

This saves significant LLM tokens (and so ⏱️+💰), both by avoiding to give extra context to your LLM on how to build/deploy, and also by avoiding LLMs actually doing the build/deploy. All the LLM needs to know is to make code changes then call the tilt MCP server to get real-time feedback.

Overview

The Tilt MCP server allows Large Language Models (LLMs) and AI assistants to interact with your Tilt development environment. It provides tools to:

• List all enabled Tilt resources
• Fetch logs from specific resources
• Monitor resource status and health
• Enable and disable resources dynamically
• Get detailed information about resources
• Trigger resource rebuilds
• Wait for resources to reach specific conditions

This enables AI-powered development workflows, debugging assistance, automated monitoring, and intelligent resource management of your Tilt-managed services.

Available MCP Capabilities

The Tilt MCP server follows the Model Context Protocol specification and exposes three types of capabilities:

🔍 Resources (Read-Only Data)

Resources provide read-only access to Tilt data. They're automatically discovered by MCP clients and can be accessed via their URI.

All resources support an optional `tilt_port` parameter (default: 10350) to query different Tilt instances.

Example URIs:

• `tilt://resources/all` - Get all resources from default port (10350)
• `tilt://resources/all?tilt_port=10351` - Get all resources from port 10351
• `tilt://resources/frontend/logs` - Get last 1000 lines from frontend (default)
• `tilt://resources/frontend/logs?tail=100&tilt_port=10351` - Get last 100 lines from frontend on port 10351
• `tilt://resources/backend/logs?filter=error` - Filter logs for errors (case-insensitive)
• `tilt://resources/backend/logs?filter=X-Request-Id:%20abc123` - Filter by request ID
• `tilt://resources/backend/describe` - Get detailed info about backend

🛠️ Tools (Actions with Side Effects)

Tools enable LLMs to perform actions that modify the state of your Tilt environment.

Read-Only Tools (for clients that don't support MCP Resources):

> Note: The read-only tools (`list_resources`, `get_resource_logs`, `describe_resource`) provide the same functionality as the MCP Resources above, but are exposed as tools for better compatibility with LLM clients (like Claude Code) that may not fully support MCP resource discovery.

All tools support an optional `tilt_port` parameter to target different Tilt instances running on different ports.

💡 Prompts (Guided Workflows)

Prompts are reusable templates that guide the LLM through common debugging and troubleshooting workflows.

Error Handling

All capabilities include comprehensive error handling:

• Resource Not Found: Raises `ValueError` with helpful message
• Tilt Connection Issues: Raises `RuntimeError` with Tilt error details
• JSON Parsing Errors: Provides detailed parsing error information

All operations are logged to `~/.tilt-mcp/tilt_mcp.log` for debugging.

Features

MCP Protocol Compliance:

• 🔍 Resources: Read-only access to Tilt data via URI templates (e.g., `tilt://resources/all`)
• 🛠️ Tools: Actions with side effects for resource management and control
• 💡 Prompts: Guided workflows for debugging and troubleshooting

Capabilities:

• 📊 Resource Discovery: List all active Tilt resources with their current status
• 📜 Log Retrieval: Fetch recent logs from any Tilt resource with configurable tail
• 🔄 Resource Triggering: Manually trigger Tilt resources to rebuild/update
• ✅ Resource Control: Enable or disable resources dynamically
• 📋 Detailed Information: Get comprehensive details about any resource
• ⏳ Wait Conditions: Wait for resources to reach specific states
• 🤖 Guided Workflows: Pre-built prompts for common debugging scenarios

Technical Features:

• 🛡️ Type Safety: Built with Python type hints for better IDE support
• 🚀 Async Support: Fully asynchronous implementation using FastMCP
• 📈 MCP Best Practices: Proper separation of resources, tools, and prompts
• 🔧 Comprehensive Logging: All operations logged to `~/.tilt-mcp/tilt_mcp.log`

Prerequisites

• Python 3.10 or higher (required by FastMCP 2.0)
• Tilt installed and configured
• An MCP-compatible client (e.g., Claude Desktop, mcp-cli)

Installation

You can install Tilt MCP in three ways:

Option 1: Using Docker (Recommended for macOS/Windows)

The Docker-based installation requires no Python setup and is automatically kept up-to-date with monthly builds. The image is optimized for size using Alpine Linux (~320MB vs 545MB+ for Debian-based images - 41% reduction).

How it works:

• Automatically discovers the Tilt API port from `~/.tilt-dev/config` based on the `tilt_port` parameter
• Uses `socat` to dynamically create a TCP tunnel from inside the container to the host Tilt server
• Your host's `~/.tilt-dev` directory is mounted with write access (Tilt CLI needs lock files)
• A single MCP server can query multiple Tilt instances by specifying different `tilt_port` values (10350, 10351, etc.)
• The Python code handles port discovery and socat management automatically

Note: The image size is primarily driven by FastMCP 2.0's dependencies (cryptography, pydantic, etc.). For reference:

• Base Alpine + Python: ~50MB
• Tilt binary: ~20MB
• FastMCP 2.0 + dependencies: ~250MB

See the MCP Configuration section below for setup instructions.

Option 2: From PyPI

pip install tilt-mcp

Best for: Linux users or when you prefer local installation

Option 3: From Source

git clone https://github.com/rrmistry/tilt-mcp.git
cd tilt-mcp
pip install -e .

Best for: Development or testing local changes

Configuration

Docker Configuration (Recommended for macOS/Windows)

Add the following to your Claude Desktop configuration file:

macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`

Windows: `%APPDATA%\Claude\claude_desktop_config.json`

Linux: `~/.config/claude/claude_desktop_config.json`

For macOS/Linux (single Tilt instance on default port 10350):

{
  "mcpServers": {
    "tilt": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "${HOME}/.tilt-dev:/home/mcp-user/.tilt-dev",
        "-v",
        "${HOME}/.tilt-mcp:/home/mcp-user/.tilt-mcp",
        "--network=host",
        "ghcr.io/rrmistry/tilt-mcp:latest"
      ],
      "env": {}
    }
  }
}

For multiple Tilt instances:

A single MCP server can query multiple Tilt instances. Simply specify the `tilt_port` parameter when calling tools or resources:

# Query resources from different Tilt instances
trigger_resource(resource_name="backend", tilt_port="10350")  # First instance
trigger_resource(resource_name="backend", tilt_port="10351")  # Second instance

# Get logs from specific instance
# URI: tilt://resources/backend/logs?tilt_port=10351

No additional configuration needed - use the same single-instance Docker config above.

For Windows (PowerShell):

{
  "mcpServers": {
    "tilt": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "${env:USERPROFILE}\\.tilt-dev:/home/mcp-user/.tilt-dev",
        "-v",
        "${env:USERPROFILE}\\.tilt-mcp:/home/mcp-user/.tilt-mcp",
        "--network=host",
        "ghcr.io/rrmistry/tilt-mcp:latest"
      ],
      "env": {}
    }
  }
}

For Windows (CMD):

Use `%USERPROFILE%` instead of `${env:USERPROFILE}` in the volume mount paths.

Key Configuration Notes:

• The `tilt_port` parameter represents the web UI port (10350, 10351, etc.) - NOT the API port
• The Python code auto-discovers the actual API port from `~/.tilt-dev/config`
• Context naming: port 10350 → "tilt-default", port 10351 → "tilt-10351", etc.
• The `~/.tilt-dev` directory must be mounted with write access (Tilt CLI needs lock files)
• `socat` dynamically forwards the discovered API port to `host.docker.internal`
• `--network=host` is required for `host.docker.internal` to work on macOS/Windows

Environment Variables:

TILT_MCP_USE_SOCAT modes:

• `auto` (default): Auto-detect based on port accessibility. Skips socat if Tilt is already reachable on localhost (e.g., Docker on Linux with `--network=host`).
• `true` or `1`: Always use socat forwarding, even if the port is already accessible.
• `false` or `0`: Never use socat, even in Docker environments.

Local Installation Configuration

If you installed via PyPI or from source, use this simpler configuration:

{
  "mcpServers": {
    "tilt": {
      "command": "tilt-mcp"
    }
  }
}

Making sure that `tilt-mcp` is in your PATH.

For Development/Testing

You can run the server directly:

python -m tilt_mcp.server

Or use it with the MCP CLI:

mcp run python -m tilt_mcp.server

Checking Version

To check the installed version of tilt-mcp:

tilt-mcp --version

Building Docker Image Locally

Build the optimized Alpine-based image:

docker build -t ghcr.io/rrmistry/tilt-mcp:latest .

Or build with a specific Tilt version:

docker build --build-arg TILT_VERSION=0.35.2 -t ghcr.io/rrmistry/tilt-mcp:latest .

To use Debian instead of Alpine (larger image but better compatibility):

docker build --build-arg BASE_IMAGE=python:3.11-slim-bookworm -t ghcr.io/rrmistry/tilt-mcp:latest .

Usage

Once configured, the Tilt MCP server provides Resources, Tools, and Prompts through the Model Context Protocol.

Using Resources

Resources are read-only and provide direct access to Tilt data. MCP clients can access them via their URI:

Get all resources:

tilt://resources/all

Returns:

{
  "resources": [
    {
      "name": "frontend",
      "type": "k8s",
      "status": "ok",
      "updateStatus": "ok"
    },
    {
      "name": "backend-api",
      "type": "k8s",
      "status": "pending",
      "updateStatus": "pending"
    }
  ],
  "count": 2
}

Get logs from a resource:

tilt://resources/frontend/logs

Returns the last 1000 lines of logs as plain text (default).

Get custom number of log lines:

tilt://resources/frontend/logs?tail=50

Returns the last 50 lines of logs as plain text.

Get detailed resource information:

tilt://resources/backend/describe

Returns detailed YAML/text output with configuration, status, and build history.

Using Tools

Tools perform actions that modify the state of your Tilt environment.

Trigger a rebuild:

{
  "name": "trigger_resource",
  "arguments": {
    "resource_name": "backend"
  }
}

Enable specific resources:

{
  "name": "enable_resource",
  "arguments": {
    "resource_names": ["frontend", "backend"],
    "enable_only": false
  }
}

Disable resources:

{
  "name": "disable_resource",
  "arguments": {
    "resource_names": ["frontend", "backend"]
  }
}

Wait for a resource to be ready:

{
  "name": "wait_for_resource",
  "arguments": {
    "resource_name": "backend",
    "condition": "Ready",
    "timeout_seconds": 60
  }
}

Using Prompts

Prompts provide guided workflows for common tasks. They generate contextual messages that guide the LLM through debugging and troubleshooting.

Debug a failing resource:

{
  "name": "debug_failing_resource",
  "arguments": {
    "resource_name": "backend"
  }
}

This generates a comprehensive debugging workflow that guides the LLM to check logs, status, and suggest fixes.

Perform a health check:

{
  "name": "health_check_all_resources",
  "arguments": {}
}

This creates a systematic health check workflow across all resources.

Optimize resource usage:

{
  "name": "optimize_resource_usage",
  "arguments": {
    "focus_resources": ["backend", "database"]
  }
}

This guides the LLM to enable only specified resources and disable others to conserve system resources.

Example Prompts

Here are some example prompts you can use with an AI assistant that has access to this MCP server:

Using Built-in Prompt Templates:

• "Use the debug_failing_resource prompt for the backend service"
• "Run a health check on all my resources"
• "Use the troubleshoot_startup_failure prompt to investigate why the frontend won't start"
• "Analyze the logs from the backend service using the analyze_resource_logs prompt"
• "Help me optimize my resources to focus on just the backend and database"

Resource Discovery & Status:

• "Show me all the Tilt resources that are currently running"
• "Which services are failing or have errors?"
• "Compare the status of frontend and backend services"
• "Access the tilt://resources/all resource to see all services"

Log Analysis:

• "Get the last 100 lines of logs from the backend-api service"
• "Read the logs from tilt://resources/frontend/logs?tail=50"
• "Show me the last 200 lines of logs from any failing services"
• "Help me debug why the frontend service is crashing by looking at recent logs"

Resource Control:

• "Disable the frontend and backend services"
• "Enable only the database service and disable everything else"
• "Enable the frontend service"
• "Disable all non-essential services to save resources"

Build & Deployment:

• "Trigger a rebuild of the backend service"
• "Rebuild the frontend and show me the logs"
• "Trigger all services that have errors"
• "Wait for the backend to be ready before checking its logs"

Advanced Automation Workflows:

• "Enable the backend, wait for it to be ready, then check its logs"
• "Disable all services, then enable only frontend and wait for it to start"
• "Get detailed info about the database and show me its recent logs"
• "Trigger a rebuild of the API service and wait until it's ready"
• "Run a complete health check and fix any issues you find"

Using Resources Directly:

• "Read tilt://resources/backend/describe to understand the configuration"
• "Compare logs from tilt://resources/frontend/logs?tail=500 and tilt://resources/backend/logs?tail=500"
• "Check tilt://resources/all to see which services need attention"
• "Get the last 50 lines from the frontend: tilt://resources/frontend/logs?tail=50"

Development

Setting up the development environment

# Clone the repository
git clone https://github.com/yourusername/tilt-mcp.git
cd tilt-mcp

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode with dev dependencies
pip install -e ".[dev]"

Running tests

pytest

Code formatting and linting

# Format code
black src tests

# Run linter
ruff check src tests

# Type checking
mypy src

Troubleshooting

Common Issues

1. "Tilt not found" error

2. "No resources found" when Tilt is running

3. Connection errors

4. Docker based tilt-mcp not able to connect

5. Alpine Linux compatibility

Debug Logging

The MCP server logs all operations to `~/.tilt-mcp/tilt_mcp.log`. The log includes:

• Server startup/shutdown events
• Resource fetch operations
• Log retrieval operations
• Error messages with full details

To enable debug logging, set the environment variable:

export LOG_LEVEL=DEBUG

Log Format: `timestamp - logger_name - level - message`

Viewing Logs:

# View recent logs
tail -f ~/.tilt-mcp/tilt_mcp.log

# Search for errors
grep ERROR ~/.tilt-mcp/tilt_mcp.log

# View logs from a specific resource fetch
grep "get_all_resources" ~/.tilt-mcp/tilt_mcp.log

Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• Setting up your development environment
• Running tests
• Submitting pull requests
• Code style guidelines

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with FastMCP for the MCP server implementation
• Integrates with Tilt for Kubernetes development

Support

• 📧 Email: aryan.agrawal@glean.com
• 💬 Issues: GitHub Issues