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analyze_knowledge_repo

Scans a knowledge repository for structural issues, duplicate files, and misplaced content, then provides a structured report with actionable cleanup steps.

Instructions

Analyse the knowledge repo for structure issues, duplicates, and misplaced files.

    Read-only — no files are modified. Scans files already inside the
    knowledge repo (inside the Docker volume), not the project source repo.
    Caches the report in memory so execute_cleanup() can act on it in the
    same session.

    To classify and migrate files from the project source repo use
    classify_documents() instead. For a simpler quality check without
    cleanup proposals use check_knowledge_quality().

    Args:
        project: Target project name (optional)

    Returns:
        Structured report with file counts by category, duplicate pairs,
        misplaced files, and numbered proposed cleanup actions (a1, a2, …)
        ready for execute_cleanup().
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavior: read-only, scans Docker volume, caches report for execute_cleanup, and details return content.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose first, then bullet details, then Args/Returns. Slightly verbose but each section adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a tool with one optional parameter. Covers behavior, return values, and integration with execute_cleanup. No gaps given no annotations or output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage. Description adds meaning for the one parameter ('project' as optional target name), but the schema already shows it's optional with a default. Adds some value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes the knowledge repo for structural issues, duplicates, and misplaced files. It distinguishes itself from siblings like classify_documents and check_knowledge_quality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use alternative tools: classify_documents() for classification/migration and check_knowledge_quality() for simpler checks. Provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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