Which integrations are available for this server?

Enables multi-agent workflows using CrewAI framework for code review, refactoring, debugging, documentation, architecture analysis, and feature development tasks Supports multi-agent orchestration capabilities through LangChain framework for intelligent code analysis and development workflows

How do I use MeshAI MCP Server?

This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.

MeshAI MCP Server

by meshailabs-org

Overview Schema Related Servers Discussions Admin

Python

Remote

MeshAI MCP Server

Build Status

A standalone Model Context Protocol (MCP) server that enables Claude Code and other MCP-compatible tools to leverage MeshAI's multi-agent orchestration capabilities.

🚀 Features

🤖 Multi-Agent Workflows: 6 pre-configured workflows for code review, refactoring, debugging, documentation, and more
🧠 Intelligent Agent Selection: Automatically selects appropriate AI agents based on task content
🔧 Framework Agnostic: Works with agents built on LangChain, CrewAI, AutoGen, and other frameworks
🐋 Docker Ready: Full Docker support with development and production configurations
📦 Easy Installation: Available as PyPI package or Docker container
🔄 Fallback Protocol: Works without official MCP package using built-in implementation

Related MCP server: Consult LLM MCP

📋 Quick Start

Option 1: Docker with stdio (Claude Code)

# Run with Docker for Claude Code integration
docker run -it \
  -e MESHAI_API_URL=http://localhost:8080 \
  -e MESHAI_API_KEY=your-api-key \
  ghcr.io/meshailabs/meshai-mcp-server:latest

# Or with docker-compose
git clone https://github.com/meshailabs/meshai-mcp.git
cd meshai-mcp
cp .env.template .env  # Edit with your settings
docker-compose up

Option 2: HTTP Server Mode

# Run as HTTP API server
docker run -p 8080:8080 \
  -e MESHAI_API_URL=http://localhost:8080 \
  ghcr.io/meshailabs/meshai-mcp-server:latest \
  meshai-mcp-server serve --transport http

# Test the HTTP API
curl -H "Authorization: Bearer dev_your-api-key" \
     http://localhost:8080/v1/tools

Option 3: PyPI Installation

# Install from PyPI
pip install meshai-mcp-server

# Run in stdio mode (for Claude Code)
export MESHAI_API_URL=http://localhost:8080
export MESHAI_API_KEY=your-api-key
meshai-mcp-server

# Or run as HTTP server
meshai-mcp-server serve --transport http --port 8080

Option 4: Development Setup

# Clone and install
git clone https://github.com/meshailabs/meshai-mcp.git
cd meshai-mcp
pip install -e ".[dev]"

# Run in development mode
python -m meshai_mcp.cli serve --dev --transport http

🔧 Configuration

Environment Variables

Variable	Description	Default	Required
`MESHAI_API_URL`	MeshAI API endpoint	`http://localhost:8080`	Yes
`MESHAI_API_KEY`	API key for authentication	None	For stdio mode
`MESHAI_LOG_LEVEL`	Logging level	`INFO`	No

🔐 Authentication

For HTTP Mode:

API Key Required: Pass via Authorization: Bearer YOUR_API_KEY header
Development Keys: Use dev_ prefix for testing (e.g., dev_test123)
Rate Limiting: 100 requests/hour for development, configurable for production

For stdio Mode:

Environment Variable: Set MESHAI_API_KEY for backend communication
No HTTP Auth: Authentication handled by Claude Code

Claude Code Integration

stdio Transport (Recommended):

{
  "servers": {
    "meshai": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "MESHAI_API_URL=${MESHAI_API_URL}",
        "-e", "MESHAI_API_KEY=${MESHAI_API_KEY}",
        "ghcr.io/meshailabs/meshai-mcp-server:latest"
      ],
      "transport": "stdio"
    }
  }
}

HTTP Transport (For hosted deployments):

{
  "servers": {
    "meshai": {
      "command": "curl",
      "args": [
        "-X", "POST",
        "-H", "Authorization: Bearer ${MESHAI_MCP_API_KEY}",
        "-H", "Content-Type: application/json",
        "-d", "@-",
        "https://your-mcp-server.com/v1/mcp"
      ],
      "transport": "http"
    }
  }
}

Local pip Installation:

{
  "servers": {
    "meshai": {
      "command": "meshai-mcp-server",
      "transport": "stdio",
      "env": {
        "MESHAI_API_URL": "${MESHAI_API_URL}",
        "MESHAI_API_KEY": "${MESHAI_API_KEY}"
      }
    }
  }
}

🛠️ Available Workflows

1. Code Review (`mesh_code_review`)

Comprehensive code review with security and best practices analysis.

Agents: code-reviewer, security-analyzer, best-practices-advisor

2. Refactor & Optimize (`mesh_refactor_optimize`)

Refactor code with performance optimization and test generation.

Agents: code-optimizer, performance-analyzer, test-generator

3. Debug & Fix (`mesh_debug_fix`)

Debug issues and generate tests for fixes.

Agents: debugger-expert, log-analyzer, test-generator

4. Document & Explain (`mesh_document_explain`)

Generate documentation and explanations with examples.

Agents: doc-writer, code-explainer, example-generator

5. Architecture Review (`mesh_architecture_review`)

Comprehensive architecture analysis and recommendations.

Agents: system-architect, performance-analyst, security-auditor

6. Feature Development (`mesh_feature_development`)

End-to-end feature development from design to testing.

Agents: product-designer, senior-developer, test-engineer, doc-writer

🌐 HTTP API Usage

Starting HTTP Server

# Using Docker
docker run -p 8080:8080 \
  -e MESHAI_API_URL=http://localhost:8080 \
  ghcr.io/meshailabs/meshai-mcp-server:latest \
  meshai-mcp-server serve --transport http

# Using pip
meshai-mcp-server serve --transport http --port 8080

API Endpoints

Endpoint	Method	Description	Auth Required
`/health`	GET	Health check	No
`/v1/tools`	GET	List available tools	Yes
`/v1/workflows`	GET	List workflows	Yes
`/v1/resources`	GET	List resources	Yes
`/v1/mcp`	POST	Execute MCP request	Yes
`/v1/stats`	GET	Usage statistics	Yes
`/docs`	GET	API documentation	No

Usage Examples

# Health check (no auth required)
curl http://localhost:8080/health

# List available tools
curl -H "Authorization: Bearer dev_test123" \
     http://localhost:8080/v1/tools

# Execute a workflow
curl -X POST \
     -H "Authorization: Bearer dev_test123" \
     -H "Content-Type: application/json" \
     -d '{"method":"mesh_code_review","id":"test","params":{"files":"app.py"}}' \
     http://localhost:8080/v1/mcp

# Get usage stats
curl -H "Authorization: Bearer dev_test123" \
     http://localhost:8080/v1/stats

🐋 Docker Deployment

Development Setup

# Development with hot reload
docker-compose -f docker-compose.dev.yml up

# Run tests
docker-compose -f docker-compose.dev.yml run --rm mcp-tests

# With mock API
docker-compose -f docker-compose.dev.yml --profile mock up

Production Considerations

For production deployment:

Use proper API key management
Set up rate limiting and monitoring
Configure HTTPS/TLS termination
Implement proper logging and metrics
Consider using a reverse proxy (nginx, Traefik)
Set resource limits and scaling policies

🧪 Development

Setup Development Environment

# Clone repository
git clone https://github.com/meshailabs/meshai-mcp.git
cd meshai-mcp

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

# Install pre-commit hooks
pre-commit install

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=src/meshai_mcp --cov-report=html

Code Quality

# Format code
black src tests
isort src tests

# Type checking
mypy src/meshai_mcp

# Linting
flake8 src tests

Building Docker Images

# Build production image
docker build -t meshai-mcp-server .

# Build development image
docker build -f Dockerfile.dev --target development -t meshai-mcp-server:dev .

# Multi-architecture build
docker buildx build --platform linux/amd64,linux/arm64 -t meshai-mcp-server:multi .

📚 Documentation

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Run the test suite
Submit a pull request

📝 License

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

🆘 Support

GitHub Issues: Report bugs or request features
Documentation: docs.meshai.dev
Discord: Join our community

🗺️ Roadmap

HTTP transport support for MCP
WebSocket transport for real-time communication
Custom workflow configuration via YAML
Plugin system for custom agents
Prometheus metrics integration
Official MCP package integration when available

Built with ❤️ by the MeshAI Labs team.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

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MeshAI MCP Server

🚀 Features

📋 Quick Start

Option 1: Docker with stdio (Claude Code)

Option 2: HTTP Server Mode

Option 3: PyPI Installation

Option 4: Development Setup

🔧 Configuration

Environment Variables

🔐 Authentication

For HTTP Mode:

For stdio Mode:

Claude Code Integration

stdio Transport (Recommended):

HTTP Transport (For hosted deployments):

Local pip Installation:

🛠️ Available Workflows

1. Code Review (mesh_code_review)

2. Refactor & Optimize (mesh_refactor_optimize)

3. Debug & Fix (mesh_debug_fix)

4. Document & Explain (mesh_document_explain)

5. Architecture Review (mesh_architecture_review)

6. Feature Development (mesh_feature_development)

🌐 HTTP API Usage

Starting HTTP Server

API Endpoints

Usage Examples

🐋 Docker Deployment

Development Setup

Production Considerations

🧪 Development

Setup Development Environment

Code Quality

Building Docker Images

📚 Documentation

🤝 Contributing

Development Workflow

📝 License

🆘 Support

🗺️ Roadmap

Tool Schema Changelog

Resources

Related MCP Connectors

Related MCP Servers

OpenAI Agents MCP Server

Consult LLM MCP

Gemini MCP

BlenderMCP

1. Code Review (`mesh_code_review`)

2. Refactor & Optimize (`mesh_refactor_optimize`)

3. Debug & Fix (`mesh_debug_fix`)

4. Document & Explain (`mesh_document_explain`)

5. Architecture Review (`mesh_architecture_review`)

6. Feature Development (`mesh_feature_development`)