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

With annotations only covering safety hints, the description carries the operational burden well: exact billing behavior (free for topped-up accounts, nothing deducted), tiered rate limits by cumulative top-up, the ai_topup_required error, and the 45,000-character ceiling with a recovery instruction. This is precisely the beyond-annotation context an agent needs.

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?

Front-loaded with the core capability, then limits, billing, and use cases in single-sentence units. Dense but nearly every clause carries operational information — no filler, though the pricing parenthetical is slightly heavy.

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?

For a tool with no output schema, the description covers errors, limits, rate limits, cost, and use cases. The only real gap is an explicit statement of the return shape, though 'OpenAI-compatible' implies the response envelope.

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 coverage is 100%, so baseline is 3; the description adds the message-length constraint (45,000 characters total) and points to GET /api/v1/models for valid model names, which is meaning beyond the schema's inline enum text.

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?

Names the specific capability — OpenAI-compatible text completions with multi-turn history — and enumerates concrete jobs (summaries, translation, sentiment labels, topic classification, structured extraction). No sibling in the list does text generation, so the resource boundary is unmistakable.

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?

Gives positive usage context ('Good for cleaning collected data: summaries, translation, sentiment labels...') and failure-driven guidance ('split long documents into several calls' on input_too_large). It does not name an alternative tool or state when NOT to use it, which keeps it short of a 5.

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