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

Beyond the readOnly/openWorld/idempotent/destructive annotations, the description richly discloses return behavior: the three possible honest response shapes, when assessment is null, the distinction between gap and not_applicable, the meaning of reachable_share, and how entity_class is derived. This is substantial behavioral context that an agent cannot infer from schema or 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?

The description is dense and long, but nearly every clause carries load-bearing meaning about response shapes, signal semantics, or edge cases. The core purpose is front-loaded in the first sentence, and the detail is necessary because there is no output schema. Some restructuring into paragraphs would improve readability, but it earns its length.

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 one parameter, rich annotations, and no output schema, the description is remarkably complete. It explains all major output branches, the coverage line, signal states, evidence grading, gap semantics, entity_class routing, and the company-only limitation. An agent has enough information to invoke the tool correctly and interpret its results.

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?

The schema already documents node_id well, including that it is from search_companies and not company_profile's entity_id. The description adds useful domain semantics on top: the node_id must refer to a company node, and person nodes will produce not_found. Since schema coverage is 100%, the baseline is 3, and this extra company-only constraint justifies a 4.

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 names a specific resource ('credit-risk view of one company') and the exact keying mechanism ('graph node_id ... the id search_companies hits carry'). It differentiates from siblings by specifying that it is not the company_profile entity_id and that persons answer not_found, so the agent knows exactly what this tool is and is not for.

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 concrete usage context: use it for company nodes with a graph node_id, and explicitly says person lookups answer not_found. It also tells the agent where the node_id comes from (search_companies) and what not to pass (entity_id from company_profile). It does not explicitly name alternatives like scoring_rubric, but the 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.