Concord MCP

Concord MCP is an open-source orchestration harness and Model Context Protocol (MCP) server developed by Get Concord AI. It provides structured task orchestration, state inspection, and inter-process monitoring for autonomous AI coding agents operating across multiple harnesses and development tools. In day-to-day development, Concord acts as an MCP server connecting MCP-compliant clients (such as Claude Desktop, Cursor, or headless agent runners) to underlying development workspaces. It exposes dedicated tools like `get_work_state`, handles parent/child task breakdowns, and manages state transitions. Developers interact with Concord through CLI commands (`concord check`, `concord watch`, `concord hook`) or allow connected LLMs to invoke its coordination tools directly during multi-step coding sessions. What distinguishes Concord MCP is its focus on multi-harness message monitoring and structured task handoffs rather than simple single-prompt code completion. It treats agent execution as a stateful, hierarchical workflow with reclaim scope feedback and lifecycle boundaries that prevent agent drift across complex tasks. Concord MCP is an open-source TypeScript project published to the MCP Registry and maintained under active community development on GitHub.

Tags: ai agent, cli, code assistant, coding agent, developer tools, OpenSource

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Key features

• Model Context Protocol (MCP) Server — Integrates into any MCP-compatible host or client to expose state and orchestration primitives. - Multi-Harness Message Monitors — Monitors and surfaces messages across multiple agent harnesses in real time. - Hierarchical Task Management — Manages parent and child task structures to partition large engineering jobs into distinct scopes. - Work State Inspection — Exposes runtime tools like get_work_state for agents to query execution progress and context. - Workflow Automation CLI — Includes CLI utilities (concord check, concord watch, concord hook) for testing and workspace observation. - Structured Handoffs — Manages lifecycle transitions and re-claim scope feedback between sub-agent execution loops.

Use cases

Use Case: Multi-Agent Task Orchestration Developers can deploy Concord MCP to orchestrate complex coding sessions where tasks are broken down into parent and child units, allowing agents to execute and hand off work items cleanly.

Use Case: Agent State and Workspace Monitoring Engineers running unattended agent tasks can use concord watch and multi-harness message monitors to observe execution progress and verify agent behavior across sessions.

Use Case: MCP Tooling for IDEs Teams using MCP-enabled IDEs like Cursor or Claude Desktop can attach Concord MCP to provide models with native task-tracking and state-querying tools.

Who it is for

Concord MCP is designed for software engineers and AI tooling developers who need an orchestration layer to manage complex, hierarchical tasks across MCP-compliant coding agents. It is not intended for users seeking a standalone, zero-config CLI chat agent that requires no external MCP client.

Install guide

Installation Install the package globally or execute it via npm: bash npm install -g @get-concord-ai/concord-mcp # or clone and build from source git clone https://github.com/Get-Concord-AI/concord-mcp.git cd concord-mcp npm install npm run build

First Run Configure Concord inside your MCP client settings (e.g., claude_desktop_config.json): json { "mcpServers": { "concord": { "command": "npx", "args": ["-y", "@get-concord-ai/concord-mcp"] } } } Run the built-in check command to verify setup: bash concord check

Example Session Start watching task monitors and event hooks in your repository: bash concord watch

Part of CLI Coding Agents

Categories: AI Coding Agents, Code Assistants, Developer Tools

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