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

Annotations already mark readOnlyHint=true, so the read-only nature is known. The description adds useful behavioral context: the score is annual, the historical series goes back to 1983, and a score below 50 indicates contracting output. It does not contradict annotations and provides interpretive value beyond the structured hints.

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 compact and front-loaded, packing the metric's scale, methodology, history, interpretation, and use cases into two sentences. Every clause earns its place; there is no filler or redundancy.

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?

For a simple read-only metric with one optional parameter, the description provides strong contextual grounding: what the score means, when to use it, and its update frequency. It does not describe the exact return structure (e.g., JSON fields), but no output schema exists; the IOM index value is mentioned, which is likely sufficient for agent invocation. Minor gaps like the absence of return format details prevent a perfect score.

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 sole parameter 'days' is fully documented in the schema, including its optionality, range (1-1825), the Gold tier requirement, and behavior fallback. With 100% schema coverage, the description does not need to repeat this information, and its absence does not hurt. The description adds no additional parameter-specific nuance, so a baseline 3 is appropriate.

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 tool returns a 0-100 US research-output momentum score based on OpenAlex publication trends and a patent-pipeline proxy. It distinguishes itself from other tools by specifying the US research/innovation domain and the composite z-score methodology, making its purpose unmistakable.

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 explicitly provides use cases: 'Call when the user asks about US research acceleration, scientific publications, R&D output, innovation or patent pipeline health, or when timing deep-tech investment screening...' This gives clear when-to-use guidance. It does not mention when-not-to-use or name alternative sibling tools, falling just short of a perfect 5.

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