Skip to main content
Glama
dl4rce

flaiwheel

by dl4rce
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

analyze_knowledge_repo

Scan the knowledge repo for structure issues, duplicate files, and misplaced content. Get a structured report with numbered cleanup actions ready for execution.

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

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

Declares read-only nature, scope (knowledge repo inside Docker volume), and caching behavior. No contradiction with missing annotations.

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 a clear intro line and bullet points; covers key aspects without excessive verbosity.

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

Completeness4/5

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

Mentions output structure and integration with execute_cleanup. Output schema covers return values. Missing prerequisites but overall adequate.

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

Parameters3/5

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

Describes the single optional parameter briefly ('Target project name (optional)'), but lacks detail on how it affects analysis. Schema has no descriptions, so description adds minimal 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?

Explicitly states 'Analyse the knowledge repo for structure issues, duplicates, and misplaced files' and distinguishes 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?

Provides specific when-to-use guidance: for classification use classify_documents, for simpler quality check use check_knowledge_quality, and notes that the report is cached for execute_cleanup.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dl4rce/flaiwheel'

If you have feedback or need assistance with the MCP directory API, please join our Discord server