mcp-agent-hub
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- AlicenseNot gradedqualityBmaintenanceEnables coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.MIT
- FlicenseNot gradedqualityCmaintenanceEnables agents and users to collaborate across rooms with shared tasks, messages, blackboards, documents, search, plugin registration, visualizations, OAuth helpers, and quantum-circuit simulation plus collaborative model training. It runs over STDIO by default, with optional HTTP/SSE deployment for container-based hosting.-
- AlicenseNot gradedqualityBmaintenanceEnables coding-agent sessions on the same machine to collaborate locally and durably by exchanging requests, replies, and task results through an authenticated loopback server with SQLite-backed storage, joined via CLI or an MCP stdio bridge. It also handles message delivery by priority and wait time, deduplicates idempotent sends, enforces task versioning, and preserves artifacts by content hash.10 npmApache 2.0
- AlicenseBqualityAmaintenanceLets multiple AI agents and parallel coding sessions share one memory and task ledger, so they can claim work instead of duplicating it, resume each other's handoffs and reasoning, and use credentials without ever seeing the keys. Connects assistants such as Claude Code, Codex, Cursor, Gemini, ChatGPT and custom agents over MCP, backed by a single local SQLite file or a self-hosted Postgres hub.35MIT
- AlicenseAqualityCmaintenanceEnables heterogeneous AI agent CLIs to coordinate via a shared SQLite-backed state bus with inter-agent messaging, shared knowledge, swarm/activity discovery, and file-level mutex locks, all without an external daemon.10MIT
- AlicenseNot gradedqualityCmaintenanceLets AI agents join a shared, local-first chat room over stdio, where they can read and post messages, wait for replies, and coordinate tasks with other models. It also exposes a common workspace so participating agents can read and write the same files while a human watches and takes part in the web UI.4 npm2MIT
TDQS
Scored across 27 tools
Most tools target distinct resources, but several boundaries overlap: broadcast vs send_message with '*', team_status vs list_agents vs hub_stats, and report_progress vs update_task vs complete_task vs escalate. The descriptions help clarify intended use, but an agent could still misselect among these related coordination tools.
Tool names consistently use snake_case and are mostly readable verb_noun patterns such as create_room, dispatch_task, send_message, and complete_task. A few names are noun-only or phrase-like (whoami, team_status, hub_stats, my_assignments), which is a minor deviation rather than a structural inconsistency.
At 27 tools, the surface is heavy for a single MCP server and exceeds the typical 3-15 well-scoped range. Some tools are convenience wrappers or overlapping views that could be consolidated, especially broadcast/send_message and team_status/list_agents/hub_stats.
The server covers the core multi-agent lifecycle: rooms, shared memory, agent identity, tasks, messaging, escalation, and stats. Minor gaps remain, such as leaving/deleting rooms, deregistering agents, or fetching a single task by ID, but agents can mostly work around these.