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Related Servers

Alternatives to project-graph-mcp

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Enables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.
      4
      MIT
    • F
      license
      A
      quality
      A
      maintenance
      Deterministic repository context packing for AI coding agents: selects, compresses, and budgets only the files a task needs. Measured 83% fewer input tokens at the same task coverage, fully local, no LLM in the loop.
      9
      8
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Persistent, compact project memory for AI coding agents, enabling them to read a small digest instead of re-scanning the codebase every session, saving tokens and costs.
      68 npm
      2
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Token-optimized semantic code search with automatic context expansion for AI coding assistants, enabling efficient discovery of code relationships and reducing token usage.
      4
      MIT

    TDQS

    B3.2/5.0

    Scored across 19 tools

    Disambiguation5/5

    Each tool targets a distinct functional area: context provision, navigation, analysis, documentation, custom rules, testing, and caching. Even similar tools like get_skeleton and get_ai_context have clearly differentiated purposes (skeleton-only vs. full context), eliminating ambiguity.

    Naming Consistency3/5

    Tool names mix single-word domain identifiers (docs, compact, db, jsdoc, navigate, analyze, testing, filters) with snake_case verb phrases (get_focus_zone, invalidate_cache, set_custom_rule). While descriptive, the lack of a uniform verb_noun or noun_verb pattern across all tools makes naming slightly inconsistent.

    Tool Count4/5

    The 19 tools are slightly above the ideal 3-15 range, but the server's broad scope (code graph navigation, analysis, documentation, testing, custom rules, AI context) justifies the count. Each tool serves a clear purpose, and no tool feels redundant.

    Completeness5/5

    The tool set provides comprehensive coverage of codebase analysis and AI assistance: navigation, quality analysis, test tracking, documentation management, custom rules, and context generation. Minor gaps like a missing delete_custom_rule are workable and do not create dead ends.

    Maintenance

    ActivityStale
    ResponsivenessNo issues