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classify_documents

Classifies project files by semantic similarity and returns a migration plan for integrating them into the knowledge base.

Instructions

Classify project repo documents for migration into the knowledge base. Read-only.

    Does not write any files. The agent reads project files locally and
    passes their content here; the Docker container cannot access the
    project source repo directly. Flaiwheel classifies each file by
    semantic similarity and returns a migration plan.

    Trigger: user says "This is the Way" or "42".
    Step 1 of the migration workflow — after classification, use the
    suggested write_*() tool for each file to push it into the knowledge base.
    Use analyze_knowledge_repo() instead when files are already inside the
    knowledge repo and need reorganisation.

    Args:
        files: JSON array of {"path": "...", "content": "..."} objects.
               Send the first ~2000 characters of each file as content.
               Example: [{"path": "docs/auth.md", "content": "# Auth..."}]
        project: Target project name (optional)

    Returns:
        Per-file classification (category, suggested write_*() tool),
        duplicate detection, and a step-by-step migration plan.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesYes
projectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, but description fully discloses read-only nature, that it does not write files, and explains the architecture (agent reads locally, Docker cannot access source repo). No contradictions.

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?

The description is well-structured with clear sections (purpose, trigger, workflow, parameters, returns) and front-loaded with main purpose. Slightly verbose but every sentence 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?

Given no annotations and a moderate-complexity tool, the description covers all key aspects: what it does, when to use, how to use, parameters, output description, and alternatives. No gaps.

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

Parameters5/5

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

With 0% schema description coverage, the description provides detailed parameter guidance: 'files' is described as JSON array with an example and truncation advice, 'project' is noted as optional target name. This adds significant value beyond the schema.

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 'Classify project repo documents for migration into the knowledge base. Read-only.' and distinguishes from sibling tools like analyze_knowledge_repo, which is for files already inside the knowledge repo.

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?

Explicit trigger condition ('user says 'This is the Way' or '42''), workflow step ('Step 1 of the migration workflow'), and alternative tool ('Use analyze_knowledge_repo() instead when files are already inside the knowledge repo').

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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