ai-coord-mcp
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- AlicenseBqualityAmaintenanceA coordination layer for coding agents that provides memorable identities, inbox/outbox messaging, searchable message history, and file lease management to prevent conflicts. Uses Git for human-auditable artifacts and SQLite for fast queries, enabling multiple agents to collaborate across projects without stepping on each other.412,228MIT
- AlicenseAqualityAmaintenanceEnables multiple AI coding-agent sessions on one machine to hand tasks to each other and get results back through a shared local folder of JSON messages, with no cloud, database, or network service. Supports direct messages, broadcasts, blocking receive, and an optional headless worker that picks up, runs, and auto-replies to tasks.6MIT
- AlicenseNot gradedqualityCmaintenanceEnables multiple AI coding agents to coordinate on a shared software project by registering, claiming tasks, declaring file intents, publishing structured change reports, and handing off context, with a local dashboard showing state in near real time.MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding providers to coordinate structured multi-agent workflows with documents, tracked tasks, handoffs, and health evidence. It owns task claims, action logs, and final health results while leaving specification files on the filesystem.29 npmMIT
- FlicenseNot gradedqualityCmaintenanceEnables two coding assistants to coordinate task creation, claiming, messages, and completion through a shared local workflow queue while using GitHub issues, branches, commits, and PRs as the source of truth.-
- AlicenseNot gradedqualityDmaintenanceA local coordination layer that enables multiple AI coding agents to share context, task leases, file ownership, and decisions via MCP tools, preventing repeated work and allowing seamless handoffs.33Apache 2.0
TDQS
Scored across 16 tools
Each tool targets a distinct resource/action: task CRUD, claiming, lifecycle transitions (complete/cancel/reopen/approve/revision), messaging, progress, and waiting. Even similar tools like list_tasks/search_tasks and claim_task/claim_next_task are clearly differentiated by purpose and described behavior.
All tools follow a consistent verb_noun pattern in snake_case (create_task, get_task, claim_task, update_progress, wait_for_task). Verb choices are semantically aligned with actions (list/get/search for reads; claim/complete/cancel for state changes), making the set predictable and easy to navigate.
At 16 tools, the set sits at the upper boundary of typical scope, but every tool serves a distinct purpose in the coordination workflow. The slightly elevated count is justified by the inclusion of specialized tools like claim_next_task and wait_for_task that optimize common operations.
The surface covers the full task lifecycle (create, read, claim, complete, cancel, reopen, approve, revision) plus messaging and progress tracking. Minor gaps exist: there's no tool to edit task metadata (e.g., title, description, assignee) after creation, but this can be worked around via cancel/recreate.