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740,141 tools. Updated 2026-11-03 17:07
"Genius" matching MCP tools:
- Validate the pr-genius installation before trusting PR analysis: checks Python/package versions, knowledge-base coverage, gh CLI, MCP registration, and sample analysis, returning warnings.MIT
- Retrieve detailed Genius song information by ID to access track metadata, artist details, and related song data for music workflows.AGPL 3.0
- Search Genius for songs and lyrics by query, returning matching tracks and lyric references for music discovery or agent workflows.AGPL 3.0
- Fetches a Genius artist profile by ID, returning artist details for use in UnClick MCP Server automation and AI agent workflows.AGPL 3.0
- Retrieve songs by an artist on Genius using their unique ID. Returns a list of songs with metadata.AGPL 3.0
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenanceEnables a persistent home world model with device graph, acoustic incident detection, and proactive maintenance proposals via MCP tools and server-sent events.MIT
- AlicenseNot gradedqualityCmaintenanceEnables searching Genius song and artist metadata, retrieving artist catalogs, and fetching annotations through the official Genius API.94 npmMIT
Matching MCP Connectors
Genius MCP — wraps the Genius API (api.genius.com)
Create and evaluate versioned decision models with reproducible results and declared assumptions.
- Find open PR case-study records—not live GitHub PRs—in the local knowledge base by repository or author for contribution strategy review.MIT
- Assess a pull request's risk before submitting to an open-source repo by analyzing repo profile, anti-patterns, and success patterns; returns risk tier, signals, checklist, and recommended action.MIT
- Retrieve all NBL seasons with IDs, names, years, season types, and start/end dates to find the season identifier for accessing stats or year-scoped data.MIT
- Assess whether a pull request is safe to submit: returns a pass/fail decision with blocking issues and recommended actions based on repo and maintainer policies.MIT
- Retrieve a repository profile to check maintainer guidelines, AI policy, merge rate, and contribution culture before submitting a pull request.MIT
- Retrieve a detailed PR case study with full frontmatter, body text, and round-by-round interaction history. Analyze past experiences to improve contribution strategy.MIT
- Enumerate the full Cartesian product for a combinatorial search space and return a cost estimate, refusing to generate a plan when the estimate exceeds your max_cost_usd ceiling.MIT
- Search anti-patterns and success-patterns by keyword to learn what to avoid and what works before submitting a PR.MIT
- Save important user preferences, decisions, and project details locally so future sessions can retrieve them by meaning instead of keywords.MIT
- Search stored memories by meaning to check prior context at the start of a conversation, finding related notes even when the wording differs.MIT
- Analyze a GitHub author's PR health data and suggest profile updates such as stale-day thresholds and response times, returning evidence and confidence scores.MIT
- Compile a maintainer action digest from PR lists to group each item by required maintainer action and export markdown for Actions or git.MIT
- Screen pull requests against repository policies to detect rule violations and get a verdict, preflight checks, and recommended actions before submission.MIT
- Assess whether a single pull request is ready to submit, then get clear fixes and next steps by mapping PR signals to contributor actions.MIT
- Retrieve recent memories to see what happened in past sessions. Use at the start of a conversation to recall what the user was working on, decisions made, and relevant context.MIT