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

Beyond the read-only/idempotent annotations, it discloses the analysis scope (competitive RPG-research, NULL awards excluded, never padded to $0), fiscal-year bounds (1985-2100, start <= end), non-paginated behavior, and how ambiguous `ic` values are handled with a `meta.ic_scope.kind` report. This is exactly the operational context annotations cannot carry.

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?

Front-loaded with the use case, then output, scope, bounds, and the `ic` caveat in a logical order; every sentence carries distinct information. It is dense and slightly long, but no sentence is filler.

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?

An output schema exists, yet the description still names the key metrics, and it covers the edge cases an agent would otherwise guess at (null handling, year bounds, fragment matching, no pagination). Nothing needed to invoke or interpret it correctly is missing.

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?

With 0% schema description coverage, the description carries the full burden and does so: `ic` is explained as either an IC abbreviation (NCI, NIGMS) or a matched name fragment spanning multiple institutes with a reported `fragment` kind, and the two fiscal-year parameters get valid range and ordering constraints. Omission of `ic` meaning all-NIH is also stated.

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 resource (NIH funding concentration across institutions) and the exact metrics returned (gini, hhi, top5_share, top-5 institutions), which cleanly separates it from siblings like rank_institutions and funding_trend. An agent can select it without opening any schema.

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?

Opens with an explicit trigger ('Use this tool when the user asks how concentrated or unequal NIH funding is...') and supplies sample user phrasings, giving clear context for selection. It does not explicitly rule out or name alternative siblings (e.g. rank_institutions) for adjacent questions, so it stops short of full when-not guidance.

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