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classify_documents

Classifies project repo documents by semantic similarity to generate a migration plan for moving 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
Behavior4/5

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

No annotations provided, so description carries full burden. It clearly states read-only nature, explains that agent reads files locally and passes content, and that Docker container cannot access source repo. However, lacks details on rate limits, auth, or error handling.

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

Conciseness5/5

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

Description is well-structured with clear sections: purpose, read-only note, trigger, step, alternative, parameter details, and return summary. Front-loaded with key info, no redundant sentences.

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 the tool's complexity (classifying multiple files), description covers workflow, input format, output (classification, duplicates, plan), and integration with other tools. Output schema exists, so return values are adequately summarized.

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?

Schema coverage is 0%, but description adds rich details: for 'files' parameter it explains format (JSON array of objects with path and content), recommends sending first ~2000 characters, and provides example. For 'project' it describes as optional target project name.

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's purpose: 'Classify project repo documents for migration into the knowledge base.' It also specifies it is read-only, step 1 of migration, and distinguishes from sibling tool analyze_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?

Explicitly provides trigger phrase ('This is the Way' or '42'), states when to use (step 1 after which write_* tools are used), and when not (use analyze_knowledge_repo() for files already in 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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