Understand why AI recommends specific players by revealing which features most influence projections using SHAP values and explainability methods.
MIT
458,158 tools. Updated 2026-08-14 23:05
"Using Goose AI for Productivity Features" matching MCP tools:
- Retrieve a detailed plan for AI-powered business context analysis, categorizing features for structured threat modeling.Apache 2.0
- Define a consistent communication protocol template to prevent AI from using different protocols for different game features, reducing bugs in multiplayer and interaction systems.MIT
- Scan a local codebase and reverse-engineer it with AI to extract structured features for VibeMap.MIT
- Retrieve GitHub releases from the Goose repository to track version updates and changelog information for development monitoring.MIT
- Retrieve all videos from the Goose YouTube channel using Content Fetcher MCP to track content updates across sessions.MIT
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- Retrieve all blog posts from Goose for content tracking and analysis within the Content Fetcher MCP server.MIT
- Create reusable Goose recipes to define specialized subagents for development tasks, enabling delegation to autonomous developer teams with custom roles and instructions.
- Create reusable Goose recipes for specialized subagents like code reviewers and security auditors, enabling AI clients to delegate tasks to autonomous developer teams with custom instructions and extensions.
- Analyze productivity patterns using AI to identify trends and optimize task management across weekly, monthly, or quarterly periods.MIT
- Automate task management and optimize workflows using AI on the IMCP server's deliberately vulnerable framework for hands-on learning and productivity enhancement.JavaScript
- Create a linear verification workflow by defining an ordered list of features for identity verification and compliance checks.MIT
- Analyze productivity and track goal progress using daily/weekly completion breakdowns, goal streaks, karma score trends, and historical karma data.MIT
- Retrieve account overview for ManyContacts WhatsApp Business CRM, including channel status, contact counts, active AI agents, and enabled features to assess account state.MIT
- Update a linear workflow's top-level settings or its flat features array. Editing features retroactively affects how past sessions' decisions are read.MIT
- List available plans and retrieve features for a selected plan, including pricing, included features, and credit rates.MIT
- Find the nearest geographic features to a coordinate, sorted by distance. Use for proximity queries and reverse lookups to identify containing features and neighbors.MIT
- Creates a new LLM configuration to power AI agents, Knowledge AI, and generative features. Requires a connection with provider credentials. Set dryRun=false to create.MIT
- Delete a Cognigy.AI large language model configuration. Features using this model stop working. Use dryRun parameter to validate before deletion.MIT
- Retrieve a unified list of all features in PingOne Advanced Identity Cloud with their install status, including IDM features and platform features like AI Agents.Apache 2.0