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

The description discloses important behavioral traits beyond the annotations: up to 4 licensed agents receive the request, marketing-partner sharing is contingent on the sell_identity scope, consent is mandatory, and every delivery/refusal is receipted. It also mentions the revocation endpoint, which is critical privacy context. These details complement the annotations without contradicting them.

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 four dense sentences, each carrying distinct information: the core action, prerequisites, scope-dependent behavior and sharing limits, and guarantees around consent and revocation. The main purpose is front-loaded, and there is no filler or redundant wording. This is an efficient, well-structured description.

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—9 parameters, a nested consent object, and no output schema—the description covers the critical elements: prerequisites, consent acquisition, dispatch limits, receipts, and revocation. It does not describe the response shape, but the mention of receipting gives a partial hint. Optional fields like street_address and best_time are not contextualized, which prevents a perfect score.

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 only 44%, but the description compensates for the most important parameters by explaining that quote_id comes from get_quotes, full_name and phone_number are the consumer's contact details, and consent must be explicitly granted by the human. It also enriches the consent semantics by explaining the consent URL flow and the meaning of the granted flag. Optional parameters like best_time and preferred_channel are not individually detailed, which is a minor gap.

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 verb and resource: 'Connect the consumer with licensed insurance agents who can quote firm and bind.' This clearly differentiates the tool from siblings like get_quotes and check_eligibility by describing the actual outcome. It also names the required input (quote_id) and the consent prerequisite, making the tool's role in the workflow 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?

The description gives strong contextual usage guidance: it requires a quote_id from get_quotes and explains scope-dependent behavior (contact_consumer vs sell_identity). This implies the tool is used after quote generation and when the user wants agent contact. It does not explicitly state edge cases or when not to use it, but the workflow context is clear enough.

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