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Glama

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Local code-graph engine for AI coding workflows: static analysis of Rust, Cangjie, ArkTS, TypeScript/JavaScript, C/C++, Python, and Shell projects into symbols, imports, call graphs, and quality gates, with pre-commit change review and impact analysis. Read-only by design — never executes build scripts or uploads code.
      6
      1
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables people and AI agents to query local code repositories as graphs through stdio MCP tools for project listing, search, dependency tracing, impact analysis, bridge detection, node inspection, bounded code reading, and optional LSP definition/reference lookups.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      MCP server for code comprehension indices and test maps. It answers what must be known to change a function and which tests pin its behavior, using compiler type information.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables querying Swift codebases as a knowledge graph, with hybrid semantic and structural search, type hierarchy, public API inspection, and code analysis through MCP tools.
      21
      Elastic 2.0
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables querying and analyzing code relationships by building a lightweight graph of TypeScript and Python symbols. Supports symbol lookup, reference tracking, impact analysis from diffs, and code snippet retrieval through natural language.
      -

    TDQS

    A4.7/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: whole-program diagnostics, module outlines, expression types, definition lookup, effect explanations, IR rendering, actor protocols, actor graphs, and context budgeting. The cross-references in the descriptions make the boundaries explicit, so an agent can reliably choose the right tool.

    Naming Consistency5/5

    All tool names follow a strict verb_noun snake_case pattern (check_file, list_definitions, infer_type, lookup_definition, explain_effect_row, render_ir_fragment, explain_actor_protocol, emit_actor_effect_graph, get_context_for_symbol, get_context_budget). Verbs are specific and the object is consistently a noun, making the naming predictable and scannable.

    Tool Count5/5

    With 10 tools, the set is well within the typical 3–15 range and each tool earns its place by covering a distinct aspect of the language analysis domain. The count feels comprehensive without being bloated; no tool appears redundant.

    Completeness4/5

    The tool set covers a broad range of analysis operations: diagnostics, symbols, types, effects, IR, actors, and context estimation. The main gaps are raw source access and cross-project symbol search, which are not provided, but the server's read-only analysis focus is otherwise well covered.

    Maintenance

    ActivityActive
    ResponsivenessNo issues