verdoc_scan

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

With no annotations, the description discloses determinism, cost, and the public repo constraint. It doesn't discuss failure modes, auth, or rate limits, but for a read-only public scan the disclosed traits are reasonably reassuring. Lacks deeper behavioral nuance like output structure edge cases.

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

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

A single, information-dense sentence that packs all key details—output fields, determinism, and cost—without redundant wording. Front-loaded with the primary purpose, making it easy to scan.

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

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

Given no output schema, the description thoroughly enumerates expected return contents (toolchain commands, phantom_paths, etc.) and adds determinism/cost context. It doesn't describe exact data structures, but that is acceptable for a fact-collection tool. The single parameter and input schema are fully covered.

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

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

Schema description coverage is 100% for the single 'repo' parameter, explicitly defining it as a GitHub 'owner/name' or URL. The description adds no new parameter details, so baseline 3 applies.

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

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

The description states a clear action and resource: 'Structured facts about a public GitHub repository' with a detailed list of content types. It distinguishes itself by highlighting deterministic, non-LLM behavior and cost, setting it apart from a typical analysis tool.

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

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

The description implies usage for deterministic repository analysis, noting 'Deterninistic; no language model' and cost. However, it does not explicitly mention when to use it over the sibling tool verdoc_agents_md, nor any exclusions or prerequisites beyond 'public GitHub repository'.

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