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

Annotations already mark the tool as readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds useful context: the data source (USASpending.gov synced data), the example output, and the analytical purpose. It does not contradict annotations and adds substance, though it omits any mention of response size or potential limitations.

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 concise and front-loaded with the primary use case. Each sentence earns its place: the usage trigger, the data returned, a concrete example, and the source citation. There is no redundancy or filler; the example is illustrative without being overly verbose.

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 covers the tool's purpose, data type, and provides a realistic output example. Given the lack of an output schema, the example is valuable. However, it does not mention data freshness, potential delays in syncing, or how the results are ordered/limited, which would enhance completeness for a data query tool.

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 input schema has only 20% description coverage (only state has a description). The description compensates by explicitly listing the filtering dimensions: 'vendor, agency, NAICS code, and fiscal year.' It also implies the vendor is the required parameter via the example. The state parameter is not mentioned, and formats (e.g., fiscal_year) are only suggested by the example, so it is not a 5.

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's verb and resource: 'Returns contract obligation data by vendor, agency, NAICS code, and fiscal year from USASpending.' It also names specific use cases (federal revenue concentration, competitor identification, vendor dependency) and provides a concrete example. This distinguishes it from sibling tools like get_vendor_contract_intelligence by emphasizing the federal/USASpending focus.

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 explicitly says 'Use when researching a company federal revenue concentration, identifying government contract competitors, or assessing vendor dependency on federal business.' This provides clear context for when to use the tool, but it does not mention alternatives or when not to use it, so it falls short of a 5.

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