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400,206 tools. Last updated 2026-08-06 08:14

"Resources for Learning or Writing Code" matching MCP tools:

  • Retrieve learning rules relevant to a code diff or pull request. Use these lessons to avoid repeating past mistakes.
    MIT
  • Retrieve full WWDC session content including transcripts, code examples, and resources for developer reference and learning.
    Apache 2.0
  • Create new projects with optional seed templates for common use cases like job search, learning, or writing. Each template adds starter tasks and knowledge files.
    MIT

Matching MCP Servers

  • A
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    An MCP service for AI-assisted reasoning and editing on long-form fiction projects. It builds a structured index from scene files for targeted context retrieval.
    Last updated
    304
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    AGPL 3.0

Matching MCP Connectors

  • Corporate travel: search and book flights, hotels, rail and transfers, manage orders.

  • Finds real, maintained open-source repos that fit your project. MCP grounding for coding agents.

  • Query structured training programs (learning paths) to retrieve metadata, enrollments, and student lists, with pagination and filters.
    MIT
  • Generate or modify code files with smart diffs. Provide file path and detailed prompt to create or edit files using context for accurate code generation.
    MIT
  • Retrieve AntV documentation, code examples, and best practices for implementation, debugging, learning, or task handling. Supports g2, g6, l7, x6, f2, s2, g, ava, adc libraries.
    MIT
  • Search SDK documentation to discover methods, parameters, and usage examples for interacting with the API. Use before writing code to find the right approach.
    MIT
  • Adds a typed memory record—learning, decision, incident, convention, or context—to project memory, capturing knowledge that should alter future behavior.
    MIT
  • Submit the final success or failure, outcome, and proof for a completed action, closing the loop for accountable fleet learning.
    MIT
  • Append a learning entry to a skill's memory to retain insights for future runs. After completing a skill, capture what failed or could be improved, then store it as a concise note tied to that skill.
    MIT