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py-ast-mcp

by gclluch
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 20 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes, but some overlap exists: `analyze_file` vs `list_functions` vs `list_declarations` all surface symbol information, and `code_smells` vs `find_errors` both detect code issues. Descriptions are detailed enough to disambiguate, but an agent might occasionally select the wrong one.

    Naming Consistency5/5

    All tool names are snake_case and follow a consistent verb-first pattern (list_*, get_*, find_*, analyze_*). A few noun-phrase names like `code_smells`, `call_graph`, and `dead_code` are exceptions, but they are still readable and fit the overall style without introducing inconsistency.

    Tool Count4/5

    With 20 tools, the server is on the heavier side of the ideal range but stays within reason for a Python AST analysis domain. Each tool covers a distinct aspect (symbols, calls, complexity, errors, docstrings, diff), so no tool feels redundant; however, the count is slightly above the sweet spot.

    Completeness5/5

    The tool surface is comprehensive for static Python analysis: it covers declarations, exports, imports, functions, methods, types, usages, call graphs, complexity, error detection, dead code, implementations, docstrings, package summaries, AST diffs, and node lookup. There are no obvious dead ends for typical analysis workflows.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

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