MeshSeeks

You can supercharge coding tasks by running a distributedMesh of specialized AI agents that work in parallel to analyze, implement, test, document, and debug your code. This MCP server orchestrates multiple Claude Code agents, coordinates their work, and provides real-time status and robust failure handling so you finish tasks faster and with better reliability.

How to use

You use this MCP server by connecting an MCP client to the local or remote server and issuing task prompts that leverage the mesh network. Typical workflows include analyzing a problem, generating an implementation plan, executing subtasks in parallel, testing, and generating documentation. You can monitor progress on the real-time status board, trigger end-to-end problem solving with multiple strategies, and inspect metrics like throughput and success rates.

How to install

Prerequisites: ensure you have Node.js 20+ installed and a compatible MCP client. You also need the Claude CLI installed and prepared for non-interactive use.

# 1. Install Node.js if you don’t have it
# Use a version manager or installer for Node.js v20+

# 2. Install Claude CLI globally and prepare permissions
npm install -g @anthropic-ai/claude-code
claude --dangerously-skip-permissions

# 3. Clone the MCP-enabled project and install dependencies
# (Adjust to your actual project path)
# git clone https://github.com/twalichiewicz/meshseeks.git
# cd meshseeks
npm install

# 4. Build the project (if applicable)
npm run build

Additional sections

Configuration and start-up are designed to be explicit and predictable. The MeshSeeks MCP server runs as a standard stdio process and is wired into your MCP client configuration. The example below shows how to wire the server into your MCP client so you can start sending tasks to the mesh network.

{
  "mcpServers": {
    "meshseeks": {
      "command": "node",
      "args": ["/absolute/path/to/meshseeks/dist/mesh-server.js"],
      "env": {
        "MCP_MESH_MAX_AGENTS": "5",
        "MESHSEEKS_CATCHPHRASE": "true"
      }
    }
  }
}

Configuration and tools

The server exposes a set of tools you can call from your MCP client to manage and operate the mesh network. The main tools are designed to help you plan and execute complex tasks across multiple agents:

• mesh_analyze_problem - Decompose complex problems into agent tasks
• mesh_execute_tasks - Execute tasks with dependency management
• mesh_solve_problem - End-to-end problem solving with multiple strategies
• mesh_status - Monitor network performance and agent metrics

Monitoring and status board

Use the real-time status board to see live updates every second, track which agents are active, visualize task dependencies, and view performance metrics like throughput and success rates. This helps you avoid perceived stalls and gives you immediate visibility into progress.

First tasks and benchmarks

You can run a quick test to verify everything is wired correctly and see parallel execution in action. Try asking the mesh to analyze a problem, then execute tasks and view the status board for real-time progress.

Troubleshooting

If you encounter timeouts or errors, review heartbeats, retries, and configuration health checks. You can adjust the heartbeat interval, maximum retries, and retry delay through environment variables documented in the configuration. Ensure the Claude CLI is accessible from your PATH or set CLAUDE_CLI_PATH to the exact executable if needed.

Available tools

mesh_analyze_problem

Decompose complex problems into agent tasks and plan execution across the mesh.

mesh_execute_tasks

Execute tasks with dependency management, coordinating multiple agents.

mesh_solve_problem

End-to-end problem solving using multiple strategies and agent collaboration.

mesh_status

Monitor network performance, agent metrics, and task progress in real time.