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

With no annotations, the description carries the full burden: it discloses that answers come with calibrated probabilities and that the model works across 19 named languages, which is useful beyond structured data. It omits practical behavior such as how many questions can be batched, latency/cost implications, determinism, or whether questions can be open-ended.

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 purpose is front-loaded in one sentence, followed by structured per-parameter definitions and compact examples, so almost every line earns its place. The formatting is dense but functional rather than padded.

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 nested objects, 0% schema coverage, no annotations and no output schema, the input side is well covered but the return shape is never described — how choice probabilities or score distributions are returned per question_id is left unclear. An agent can construct a call but cannot confidently predict the response format.

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 0% and 'state' has only a title, so the description does the heavy lifting: it defines state as a string or JSON record and documents the questions map with three question types and concrete example payloads. It still leaves gaps such as how question_id keys map to results and any constraints on criteria keys or list ordering.

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 action — answering typed questions about a message, ticket, record or form — and returns a calibrated probability, which is distinctive versus siblings like extract_document or generate_questions. It never explicitly names or contrasts those siblings, so an agent must infer the boundary rather than being told.

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?

Usage is only implied through the examples of question types; there is no explicit 'use this when' or 'use X instead' guidance, and no exclusions relative to generate_questions or extract_document. The three question-type examples do make the intended application reasonably inferable.

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