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

Annotations already declare read-only and closed-world, so the description's value lies in disclosing data-quality behavior: results carry a reported outcome plus an evidence label identifying the claimant, and quoting requires verification via get_use_case. That is meaningful context an agent could not infer from 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 short sentences with no filler: the first states what is returned, the second states the required next step. The critical provenance caveat is placed last, but everything earns its place.

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?

Six optional parameters and no output schema, yet the description conveys the shape of a result (outcome plus evidence label) and the verification workflow. Gaps remain around matching behavior, result ordering, and the effect of limit, but the essentials for correct invocation are 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 only 50% — category, industry, and limit have no schema descriptions — and the description adds no information about any parameter, not even how query matches or how evidence filtering interacts with the result labels. The description does not compensate for the coverage 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?

States a specific verb and resource ('Search real, sourced AI agent deployments at named organizations') and distinguishes itself from the sibling get_use_case by describing results as search hits and routing detail lookup to get_use_case.

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 an explicit follow-up rule ('Call get_use_case for sources and caveats before quoting a result'), which is real workflow guidance. It does not state when to prefer this search over get_dataset_stats or what to do when a query returns nothing, so it stops short of full when/when-not coverage.

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