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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, covering safety and mutability. The description adds valuable behavioral context beyond those: it explicitly defines the fiscal year (Oct–Sep) and warns that fiscal month 1 is October, not January—critical for correct interpretation of results. It also discloses the return structure (per-period totals and optional award-category breakdowns), which is not present in the annotations. No contradictions exist.

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 three sentences with no filler. The first sentence front-loads the core action and grouping options; the second adds a critical nuance about the fiscal year; the third explains filters and output shape. Every sentence earns its place, and the structure is logically ordered from purpose to caveat to usage, keeping it tight and skimmable.

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?

Given the tool's moderate complexity (3 parameters, one nested object, and an output schema), the description covers the essential usage context: the main grouping axis, the fiscal-year caveat, the filter dimensions, and the output shape. It does not mention defaults (e.g., award_type_codes defaulting to contracts) or the subawards toggle, but those are fully documented in the schema. Since the output schema exists, the description need not explain return values beyond what it already does. Overall, it is complete enough for an agent to invoke correctly without external documentation.

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 the schema already documents the 'group' enum, filters (keywords, agency_name, naics_codes, recipient_id, time_period_start/end, award_type_codes), and the 'subawards' flag. The description's mention of filter types and grouping parallels the schema without adding technical detail beyond what is already present. The fiscal-year clarification is useful but also exists in the schema's description for the 'group' parameter, so the description does not elevate understanding beyond the baseline for fully-covered schemas.

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 primary action ('Fetch aggregated federal obligation amounts') and the exact grouping axis ('by fiscal year, fiscal quarter, or fiscal month'). It also specifies the filter dimensions (award type, agency, recipient, keyword, NAICS code), which distinguishes it from time-less aggregation tools like spending_by_category or spending_by_geography. The purpose is unmistakable and immediately differentiates the tool from its siblings.

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 implies usage for tracking spending trends over time ('to trace spending trends in a specific area') and clarifies fiscal-year semantics, which helps avoid misuse. However, it does not explicitly contrast itself with sibling tools (e.g., 'use this for time series, use spending_by_category for category breakdowns'). No when-not-to-use guidance is provided, so the agent must infer when this tool is preferable to alternatives.

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