Rutherford MCP Server
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- FlicenseNot gradedqualityNot gradedmaintenanceOrchestrates multiple AI models (Gemini, OpenAI, Claude, local models) within a single conversation context, enabling collaborative workflows like multi-model code reviews, consensus building, and CLI-to-CLI bridging for specialized tasks.-
- 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
- AlicenseNot gradedqualityCmaintenanceEnables multiple AI coding CLIs (Claude Code, Gemini/Antigravity, Codex, and OpenCode) to collaborate as a coordinated team by routing cross-agent prompts, sharing messages and review tickets, tracking tasks on a shared store, and isolating each agent in its own Git worktree with turn-budget safeguards—all inspectable and steerable from a local web dashboard.MIT
- AlicenseAqualityDmaintenanceOrchestrates multiple Claude Code agents across iTerm2 sessions with process-level isolation, enabling collaborative AI development workflows on multiple codebases with task-based inter-agent communication and persistent state management.71MIT
- AlicenseCqualityBmaintenanceEnables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.1641 npmMIT
- AlicenseBqualityBmaintenanceEnables any MCP host to delegate work to multiple coding-agent CLIs such as Codex, Claude Code, and Antigravity as subagents, preserving native sessions and supporting team-based supervision and agent-to-agent messaging.17139 npmApache 2.0
TDQS
Scored across 18 tools
Most tools target clearly distinct actions: delegate runs one agent, consensus runs many in parallel, debate runs an argument across rounds, and the job tools each cover a different lifecycle stage. However, review and plan are documented as thin wrappers over consensus and delegate respectively, which creates some purpose overlap that agents must be careful to disambiguate.
Multi-word tool names consistently use snake_case verb_noun or noun_noun patterns (list_jobs, cancel_job, reload_panels, job_result), and single-word names are short readable verbs or nouns. Minor inconsistencies exist, such as 'capabilities' being a bare noun alongside 'list_roles'/'list_jobs', and 'activity' being a noun where 'list_activity' would fit the pattern.
At 18 tools, the count sits at the heavier end, but the server spans a broad domain: orchestration actions (delegate, consensus, debate, review, plan), a full background-job subsystem (5 tools), and agent/config management (5 tools). Each tool earns its place for this scope; the count is justified rather than bloated.
The core lifecycle is well covered: task execution (delegate/consensus/debate), the complete job lifecycle (create async, poll status, fetch result, cancel, continue, list all/active), and agent management (capabilities, doctor, discover, setup). Minor gaps remain — panels are edited via files with no creation tool, and there is no way to explicitly delete kept runs beyond TTL eviction.