format_question

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

Although no annotations are present, the description discloses that the tool never throws exceptions and degrades to markdown block on failure, and mentions the return structure. This is adequate for a formatting tool but could be more explicit about idempotency or side effects.

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?

The description is concise (5 sentences) and front-loaded with the core purpose. Every sentence adds value without fluff, though it could be slightly more organized for readability.

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 the tool's complexity, the description covers purpose, input rules, parameter semantics, return format, and integration with ingest_question. It does not include examples or edge cases, but is mostly complete for a formatting tool with an output schema.

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?

With 0% schema description coverage, the description fully compensates by explaining the meaning of question_type (with a dictionary reference), the condition for options (non-empty only for multiple choice), and the expected markdown content for stem, adding significant value beyond the schema.

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 clearly states the tool converts natural markdown stem and options array into blockJson for unified rendering across clients, with specific details on input content rules. It distinguishes itself from sibling ingest_question by explaining the output is to be fed to that 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 provides clear context on when to use (formatting questions before ingestion) and guides the AI on minimal content to produce (markdown with sub-questions, images, LaTeX, etc.). It does not explicitly mention alternatives or when not to use, but the context is sufficient for a specialized formatting tool.

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