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

Annotations only declare readOnlyHint=true, so the description adds valuable operational context: each record costs 1 credit, pagination uses scroll_token, and the API endpoint is POST /company/search. These details go beyond what the annotations provide.

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 three compact sentences, each earning its place: purpose and query modes, pagination mechanism, and cost/endpoint. The most important information is front-loaded.

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?

The description provides enough to invoke the tool correctly: search modes, pagination, cost, and endpoint. Since there is no output schema, it could have mentioned the response shape more explicitly, but scroll_token's origin is inferable from the schema.

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 100%, so the baseline is 3. The description adds useful context by framing query and sql as alternative modes and highlighting scroll_token for pagination, but it does not deeply elaborate on parameter behavior beyond the schema.

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 clearly states the verb 'Search' and the resource 'full PDL Company Dataset', and specifies two query modes: Elasticsearch query object or SQL string. It distinguishes itself from person-focused siblings, though it does not explicitly name sibling tools.

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 intended use is implied: search the full company dataset using ES/SQL or paginate with scroll_token. However, there is no explicit guidance on when to choose this over pdl_company_enrich, pdl_autocomplete, or other sibling tools, and no exclusions are stated.

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