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Alternatives to decisions-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Indexes codebases and lets AI agents retrieve precise code snippets (functions, classes, routes) instead of reading entire files, reducing token usage and improving accuracy.
      85 npm
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables coding agents to search a project's code and receive the most relevant source excerpts with exact line numbers, so agents spend fewer tokens, tool calls and rounds locating the code behind a question. Search runs locally, with only the question and selected snippets sent to a relevance ranking service.
      14
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI coding agents to quickly find relevant code, focus reads on important parts of large files, and pick relevant items from long lists, reducing token usage and search time.
      7
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Adds semantic code search to AI coding agents, enabling natural language queries across entire codebases to retrieve relevant code chunks, saving tokens and providing deep context.
      41 npm
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI coding agents to locate code by meaning rather than text patterns, combining embedding-based semantic search, exact/regex literal matching, and tree-sitter AST structural filters to return compact, tree-shaken code context. It also supports symbol metadata filtering, duplicate detection, codebase surveys and clustering, call-graph exploration, and exhaustive changed-file audits.
      2
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI coding agents to perform private, local code search that returns only the most relevant code chunks, reducing token use and setup while keeping repositories on the user's machine.
      MIT

    TDQS

    A3.9/5.0

    Scored across 4 tools

    Disambiguation4/5

    locate_in_files and list_models are clearly distinct, but classify_files and decide_files overlap: classification is effectively a one-question decision, so an agent must read carefully to know which to pick for a single-label vs multi-question pass. The descriptions do clarify the intended boundary (grouped label output vs typed yes_no/choice/score answers), so confusion is limited rather than pervasive.

    Naming Consistency5/5

    All four tools are snake_case verb-first names (classify_files, locate_in_files, decide_files, list_models) with a predictable verb + target pattern. list_models is the only one not scoped to 'files', but it follows the same convention and is semantically clear.

    Tool Count5/5

    Four tools is a tight, well-scoped surface: three file-analysis operations plus a model-discovery utility that enables the others. Each tool has a distinct role and none feels redundant or bolted on.

    Completeness4/5

    The core workflow of triage (classify), structured interrogation (decide), and pinpointing (locate) is covered, with list_models to support model choice. Minor gaps exist: no direct way to inspect raw file contents outside results, no aggregation/summary across the outputs of multiple passes, and no mechanism to persist or reuse a named question set.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing