A
licenseNot graded
qualityD
maintenanceEnables multi-model leader-worker agent orchestration, workflow execution, and deterministic validation via structured MCP tools.
7 npm
Apache 2.0
ai-collab-mcp
Related Servers
Alternatives to ai-collab-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to delegate tasks to multiple local and cloud AI workers (Codex, Claude Code, DeepSeek, GLM, Gemini) with deterministic routing, concurrent batch execution, idempotent retries, and auditable traces.MIT
- FlicenseNot gradedqualityAmaintenanceEnables multi-agent AI collaboration by connecting Claude Code, OpenAI Codex, and Google Antigravity, providing cross-model code reviews, independent second opinions, and autonomous validation through MCP tools.1-
- AlicenseBqualityFmaintenanceEnables AI assistants to intelligently select and switch between different AI models (OpenAI, Anthropic, etc.) within the same conversation based on task requirements. Provides a unified interface for accessing multiple AI providers through a single MCP tool.116 npmMIT
- AlicenseAqualityCmaintenanceEnables multi-agent collaboration across different AI assistants and projects by providing a universal coordination layer for MCP-compatible agents to communicate, share context, and coordinate complex tasks seamlessly.1223 npm33MIT
- AlicenseBqualityFmaintenanceEnables orchestrating multiple AI CLI agents (Claude Code, Codex, Gemini CLI, Copilot CLI) through a unified MCP interface for task delegation, cross-agent comparison, and specialized tools like code review and debugging.143 npm14MIT
TDQS
A4.2/5.0
Scored across 6 tools
Disambiguation5/5
The tools split clearly into discovery (list_providers, list_agents, list_models) and execution (run_pipeline, run_orchestrator, run_debate). Each tool targets a distinct resource or workflow style, so an agent is unlikely to select the wrong one.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: list_* for discovery operations and run_* for execution modes. This makes the tool surface predictable and easy to navigate.
Tool Count5/5
Six tools is a well-scoped size for an AI collaboration server. The three list tools support the three run tools without unnecessary duplication or overwhelming sprawl.
Completeness5/5
The surface covers the full workflow: discovering available providers, agents, and model capabilities, then executing collaboration via pipeline, orchestrator, or debate. Configuration management appears intentionally out of scope rather than a missing capability.
Maintenance
ActivityMaintained
ResponsivenessNo issues