crew-mcp
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- AlicenseNot gradedqualityAmaintenanceEnables Claude Code to hand off bulk, mechanical, read-heavy tasks to a local model, including agentic loops that can read, write, and run commands sandboxed at zero cloud token cost.MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude Code and other MCP clients to hand bounded work — code review, test stubs, commit messages, docs, format conversion, mock data, embeddings — to a local GPU model or a cheap cloud/OpenAI-compatible endpoint, so file contents are sent straight to that model instead of entering the main AI platform's context window. This cuts token spend dramatically while giving users a second model for reviewing code, with the orchestrating assistant still checking every result.MIT
- AlicenseNot gradedqualityAmaintenanceGives ChatGPT and Claude Code policy-scoped access to a local repository checkout, allowing them to list, search, read, edit, and create files, run operator-approved fixture tests, and inspect git diffs. It confines operations to configured paths and resources, and supports an independent read-only review workflow.2 npm4MIT
- AlicenseAqualityBmaintenanceEnables Claude Code to submit code, diffs, plans, and security or edge-case questions to Codex, Gemini, and GPT-OSS for read-only review, verifying each quoted line against the real files so fabricated findings are filtered before Claude reads them. It also runs multi-family councils that surface agreement, falls back across models when quotas run out, and supports optional hooks that block git push until edits have been reviewed.104MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude to offload noisy commands, large files, and web research to a local OpenAI-compatible LLM, receiving only concise cited answers instead of the raw material. It also optionally delegates writing a single file inside an isolated git worktree, retrying until a Claude-authored test passes.02MIT
- FlicenseNot gradedqualityAmaintenanceEnables Claude Code and other MCP clients to offload bounded work—code review, test stubs, boilerplate, commit messages, format conversion, embeddings—to a local GPU model or a cheaper cloud, OpenAI-compatible endpoint. Source files are read by the server rather than the main model, cutting token spend while the orchestrating model keeps planning and verifying.-
TDQS
Scored across 6 tools
Each tool has a distinct role: recon maps code, review_diff audits working-tree changes, second_opinion critiques plans/diffs, check_citations verifies file:line refs deterministically, stats reports telemetry, status reports config. The only real overlap is crew_review_diff vs crew_second_opinion, both of which can consume the current diff, but the descriptions clearly separate a local scout bug review from an independent model reviewer.
All six names share the crew_ prefix and snake_case, giving a predictable namespace. Minor deviation: most are verb_noun (review_diff, check_citations, second_opinion) while recon, stats and status are bare nouns, but the pattern remains readable and unambiguous.
Six tools is well within the well-scoped range and each earns its place: three work tools (recon, review, second opinion), one deterministic checker, and two introspection tools (status, stats). No tool looks like filler or a near-duplicate.
The surface covers the full delegation lifecycle for its stated purpose: check availability (status), gather context (recon), review (review_diff, second_opinion), verify claims (check_citations), and audit usage (stats). Minor gaps exist around configuring workers or cancelling long-running jobs, but agents can work around these with a status call.