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  • A
    license
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    quality
    B
    maintenance
    An MCP server that reduces AI coding agent token usage by 80-99% through a queryable knowledge graph of code, change tracking, and persistent memory across sessions.
    75 npm
    3
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Compiles codebases into a layered knowledge graph, providing an MCP server with 16 tools for AI to navigate, understand, and analyze code before making changes.
    2
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    A navigable symbol map of your codebase for coding agents, enabling efficient code navigation and reading only relevant symbols instead of entire files.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables agents to generate a JSON sitemap of how a path through a TypeScript/JavaScript codebase runs, capturing the summary, areas, every step with its file, lines, description, calls and data, and the links between services. The resulting document can be opened in a viewer that draws the same map as a single page, and rebuilt in CI to keep system documentation current.
    576 npm
    MIT
  • A
    license
    C
    quality
    D
    maintenance
    Enables code analysis of JavaScript, TypeScript, TSX, Python, and Rust files using Tree-sitter, providing symbol listing, definition/reference lookup, AST queries, LSP-style features, and SQLite indexing for efficient cross-file searches.
    21
    3 npm
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables sandboxed file operations via MCP tools, resources, and prompts, with a Claude CLI client and Groq-powered web UI for file CRUD, search, code review, and documentation generation.
    MIT
  • A
    license
    C
    quality
    D
    maintenance
    A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.
    3
    4 npm
    1
    ISC
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.
    Apache 2.0
  • A
    license
    A
    quality
    C
    maintenance
    Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.
    3
    Apache 2.0
  • A
    license
    A
    quality
    B
    maintenance
    Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.
    18
    29 PyPI
    3
    MIT
  • A
    license
    C
    quality
    D
    maintenance
    AI development assistant with 36 specialized tools for memory management, code analysis, project planning, and problem-solving through natural language interactions in Korean and English.
    36
    3
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Exposes read-only tools for OPC UA PLC tag monitoring and diagnostics, and enables template-driven, validated IEC 61131-3 SCL code generation with a mandatory human review gate before download.
    1
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server that generates, runs, and triages tests by introspecting Python modules or web pages, using structured LLM outputs for scenario generation and failure analysis.
    1
    MIT