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260,856 tools. Last updated 2026-07-05 08:54

"Using local LLMs for code writing, reviewing, and rule generation" matching MCP tools:

  • 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 author-written Agent Skills for an LPM package, including usage patterns, anti-patterns, gotchas, and best practices for code generation. Skills are version-specific and resolve from local package.json.
    ISC
  • Retrieve system environment details including OS, Python version, installed libraries, and execution mode before generating code. Helps tailor code to the current setup and avoid errors.
    MIT
  • Learn to structure prompts for Veo video generation to achieve higher quality results. Get examples and tips for effective prompt writing.
    MIT
  • Retrieve test scenarios for specified components. Use for writing tests or reviewing implementations against the spec. More efficient than full spec retrieval when only tests are needed.
    MIT
  • Get structured guidance for writing prompts that produce optimal video generation results. Includes examples and tips for effective prompt structure.
    MIT

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  • Generate a unified chronological timeline merging oncology documents and conversation entries for reviewing medical history, sharing with doctors, or writing patient journals.
    MIT
  • Explain code snippets by answering specific questions about them, using a local model to avoid token consumption and preserve Claude's context for complex reasoning.
    MIT
  • Retrieve project conventions, patterns, templates, and rules before writing code. Ensures generated code adheres to project-specific guidelines.
    MIT
  • Implement code using strict Test-Driven Development methodology by writing tests before production code. Supports iterative TDD cycles with session resume functionality.
    MIT
  • Chat with local LLMs using conversation history, system messages, tool calling, and adjustable generation settings.
    AGPL 3.0
  • Generate text completions for code and content using vLLM models. Provide prompts to produce coherent outputs with configurable parameters like temperature and token limits.
    Apache 2.0
  • Retrieve daily status report details—including project, hours, and description—for any employee by their numeric ID. Useful for reviewing work history before writing status reports.
  • Retrieve language-specific testing guidelines including naming conventions, structure, assertions, mocking approaches, and coverage requirements before writing or modifying test code.
  • Analyze text for grammar, spelling, and punctuation errors with local rule-based detection. Returns structured errors, corrections, and explanations tailored to the writer's native language.
  • 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