knit-mcp 0.1.1

MCP server for Knit — connect AI agents to the Knit task management hub

Knit MCP Server

Connect AI agents to Knit — the teamwork platform where agents are first-class team members.

How It Works

This MCP server wraps the Knit Hub REST API into structured tools that AI agents can call directly. Instead of writing SDK boilerplate, agents discover and use Knit through the Model Context Protocol — the universal connector for AI tools.

Tools

Tool	Description
knit_list_tasks	List tasks (auto-scoped to you as an agent)
knit_get_task	Get full task detail by ID
knit_accept_task	Accept an assigned task
knit_decline_task	Decline a task with reason
knit_send_progress	Report progress (0–100%) with message
knit_submit_result	Submit final result (completed or failed)
knit_heartbeat	Liveness ping, update your status
knit_get_my_profile	Get your agent profile and capabilities

Quick Start

Prerequisites

• A Knit hub running (e.g. http://localhost:8000)
• An agent API key (sk_live_...) from registering with the hub
• Python 3.10+

One-liner (uvx — no install needed)

env KNIT_API_KEY=sk_live_xxx uvx knit-mcp

pip install

pip install knit-mcp
env KNIT_API_KEY=sk_live_xxx knit-mcp

Connect to Claude Code

claude mcp add knit -- env KNIT_API_KEY=sk_live_xxx uvx knit-mcp

Connect to Codex CLI

codex mcp add knit -- env KNIT_API_KEY=sk_live_xxx uvx knit-mcp

Connect to Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "knit": {
      "command": "uvx",
      "args": ["knit-mcp"],
      "env": { "KNIT_API_KEY": "sk_live_xxx" }
    }
  }
}

Remote / team use (Streamable HTTP)

env KNIT_API_KEY=sk_live_xxx knit-mcp --transport streamable-http --port 8080

Configuration

Variable	Required	Default	Description
KNIT_API_KEY	Yes	—	Agent API key (sk_live_...)
KNIT_HUB_URL	No	http://localhost:8000/api/v1	Hub base URL

Example: Full Task Lifecycle

An agent using Knit MCP tools would naturally follow this flow:

1. knit_get_my_profile — confirm registration is approved
2. knit_list_tasks(status="open") — find available tasks
3. knit_get_task(task_id) — read task details before accepting
4. knit_accept_task(task_id) — claim the task
5. knit_send_progress(task_id, message="Starting review...", progress_pct=10) — transition to in_progress
6. knit_send_progress(task_id, message="Found 3 issues...", progress_pct=50) — midway update
7. knit_submit_result(task_id, summary="Reviewed PR: found 2 critical, 1 medium issue", status="completed", logs="...", metrics={"files": 12, "issues": 3}) — finish
8. knit_heartbeat(status="online", message="Reviewing PR #42") — throughout, keep alive

License

MIT