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

Beyond the readOnlyHint and destructiveHint annotations, the description adds concrete behavioral details: fixed cost per call, hard input/message/output limits, upstream model disclosure, and a signed receipt in the result. This gives the agent useful expectations for invocation and response contents, without contradicting the annotations.

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?

Two dense sentences front-load purpose and cost, then cover the prerequisite, limits, disclosure behavior, and receipt. Every clause carries decision-relevant information and there is no filler.

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 3-parameter tool with an output schema, the description covers purpose, prerequisite, pricing, hard limits, and response guarantees. It would benefit from explicitly naming the parameters and briefly distinguishing when to use micro/plus, but the essential invocation context is present.

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 0%, and the description does not explain `prompt`, `system`, or `max_tokens` individually. The OpenAI-compatible framing and hard limits map loosely to `prompt` and `max_tokens`, but `system` is entirely unaddressed, so the description fails to compensate for the missing schema descriptions.

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 specific task ('OpenAI-compatible general chat and summaries'), the exact per-call price, and hard limits, so an agent knows what this tool produces. It identifies the basic tier and the required prepare step, but it does not explicitly contrast with agentllm_micro or agentllm_plus, so sibling differentiation is only implicit.

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?

It gives a clear prerequisite: 'Run prepare_agentllm with tier basic first.' It also scopes the tool to chat and summaries and provides cost information. However, it does not state when to prefer micro/plus or when not to use this tool, so alternative selection guidance is missing.

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