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    • A
      license
      B
      quality
      B
      maintenance
      Enables LLMs to efficiently read, write, and refactor code using precise AST-based operations, reducing token usage and context window waste.
      25
      34 npm
      3
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides file caching and diff tracking for AI coding agents, reducing token usage by returning changes or confirming no changes instead of full file contents on repeated reads.
      35 npm
      220
      MIT
    • A
      license
      A
      quality
      D
      maintenance
      Provides intelligent code context and analysis through semantic compression, AST parsing, and multi-language support. Offers 60-80% token reduction while enabling AI assistants to understand codebases through local analysis, OpenAI-enhanced insights, and GitHub repository integration.
      6
      3 npm
      3
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI coding agents to query a pre-built semantic knowledge graph of code, reducing token usage and tool calls. Supports 16 tools for code exploration, analysis, and context building.
      16 npm
      8
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.
      5
      58 npm
      63
      TypeScript
      MIT

    TDQS

    A4.6/5.0

    Scored across 14 tools

    Disambiguation4/5

    Tools are largely distinct: read vs batch_read (one vs many), edit vs batch_edit (one vs many), edit vs edit_preview (apply vs probe), search (BM25 concept) vs grep (exact/regex), read (text) vs read_image (images). The main soft spot is the discovery cluster (warm, glob, search, grep), which all deal with cached files, but the descriptions draw clear boundaries between indexing and searching.

    Naming Consistency4/5

    Names are consistently snake_case and mostly verb-led (read, write, edit, delete, search, warm, grep, clear), with predictable batch_ and edit_preview qualifiers. Minor deviations are the noun-style names stats and glob, which break the verb pattern but remain readable and unambiguous.

    Tool Count5/5

    14 tools sit comfortably in the well-scoped 3-15 range, and each earns its place by covering a distinct slice of file I/O plus cache management. No redundant or filler tools are present.

    Completeness4/5

    Core lifecycle is covered: create (write), read (read/batch_read), update (edit/batch_edit), delete, plus discovery (glob/grep/search/warm) and diagnostics (stats/clear). Gaps like directory creation, move/rename, and symlink operations are minor and workable, but agents needing those will hit dead ends.

    Maintenance

    ActivityNo data
    ResponsivenessNo issues