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Refactory

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      Enables developers and coding agents to refactor Python codebases by re-aliasing imports and migrating dependencies, safely rewriting module paths and callsites through deterministic AST analysis instead of error-prone regex. It also surfaces dependency graphs and refactoring plans that minimize LLM token usage.
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    TDQS

    A3.8/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool has a distinct and clearly defined purpose within the decomposition pipeline—analyze, characterize, decompose, depmap, extract, fix imports, metrics, plan, report, verify, verify exports. There is no overlap or ambiguity between them; an agent can easily select the correct tool for each step.

    Naming Consistency5/5

    All tools follow the consistent pattern 'refactory_verb_noun' (e.g., refactory_analyze, refactory_fix_imports, refactory_verify_exports). The naming is uniform, predictable, and uses only snake_case, making it easy for an agent to infer functionality from the name.

    Tool Count5/5

    With 11 tools, the server is well-scoped for its domain of code decomposition. Each tool serves a necessary step in the pipeline, and the count is neither too low (missing functionality) nor too high (unwieldy). The tool set earns its place.

    Completeness5/5

    The tools cover the entire decomposition lifecycle: analysis, characterization, dependency mapping, planning, extraction, import fixing, verification, metrics, reporting, and export verification. There are no obvious gaps; the surface is complete for the stated purpose of refactoring a monolithic file.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues