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

With no annotations, the description carries the burden. It discloses pricing ($0.09 via x402), the nature of outputs (value, urgency, why_it_matters, recommended_action), and the data source. It omits edge-case behavior like empty results or pagination, but the key behavioral traits are covered.

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 two sentences with no wasted words. It front-loads pricing, purpose, output structure, and filters, and the second sentence clarifies the tool's position. Every clause earns its place.

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 inputs (sector, state), output structure, cost, and data source, which is substantial for a tool without an output schema. It lacks information about error behavior or what happens when no signals are found, but it provides a solid overview.

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 coverage is 75%, so the schema documents most parameters. The description adds filtering semantics for sector and state but doesn't mention months or x_payment beyond the cost reference. It mainly reinforces schema rather than adding substantial new meaning.

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 provides scored, agent-actionable opportunity intelligence signals from US federal spending. It distinguishes itself from a raw feed, implying a value-add layer, and the resource (federal spending signals) is specific.

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 clear context on when to use: when agents need real-world money signals, and explicitly says it is not a raw feed, implying the alternative is raw data. It doesn't name specific sibling tools but provides enough context for differentiation.

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