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

Annotations already declare readOnly, idempotent, closed-world, non-destructive behavior, so the description's job is to add context, which it does: it discloses that no Research API read occurs, that cases are versioned descriptions of implemented workflows rather than observed market evidence, and that output is structured help. It does not, however, discuss error or rate-limit behavior.

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 front-loaded with the primary use case and then segmented into labeled Selection, Presentation, and Evidence blocks, so an agent can scan it. It is dense but nearly every sentence carries operational content; only the 'Presentation' guidance edges toward advisory rather than instructional.

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?

With an output schema present, return values need not be re-explained, and the description instead covers usage, selection behavior, and the nature of the returned cases. That is close to complete for a five-parameter discovery tool among 27 siblings, though it never routes to specific sibling tools for the follow-up actions it describes.

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 baseline is 3, but the description adds real semantics beyond the schema: all query words must match, an empty query lists the whole area, and keywords should be broadened if a result set is empty. This meaningfully clarifies how 'area', 'query', and 'use_case_id' interact.

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 function: resolving which workflow, metric, widget or control serves a question, and serving users who ask what can be investigated. It distinguishes itself from the many research_show_* siblings by declaring it returns 'structured help, not a market result' and by excluding rendering another widget.

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?

It gives explicit when-to-use conditions ('user asks what can be investigated', resolving which workflow serves a question) and an explicit when-NOT-to-use list ('live data availability, mandatory preflight before familiar tools, general financial definitions already understood, or rendering another widget'). It also gives selection guidance for choosing between filtering modes.

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