get_awardGet full detail for one federal award from USAspending.gov by its generated award id (from search_awards results, e.g. "CONT_AWD_...") - description, obligations, period of performance, awarding/funding agencies, recipient, and place of performance. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| award_id | Yes | generated_internal_id / generated_unique_award_id from search_awards |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds value by explicitly disclosing 'Keyless' (no authentication required) and enumerating the returned data categories (description, obligations, period of performance, agencies, recipient, place of performance), which are not in the annotations.
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 a single, well-structured sentence that front-loads the action and resource, then efficiently lists the included data fields. No redundant words or filler; every clause contributes to understanding the tool's purpose and output.
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?
With one parameter, full schema coverage, and no output schema, the description carries the burden of explaining return content. It lists the major field groups and notes the keyless nature, which is sufficient for an agent to understand the tool's scope and expected output.
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 schema already describes award_id as 'generated_internal_id / generated_unique_award_id from search_awards' with 100% coverage. The description adds a concrete example ('CONT_AWD_...') and reiterates the source context, providing a small but useful semantic boost beyond the schema.
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 verb ('Get'), the resource ('one federal award from USAspending.gov'), and the input key ('generated award id'), with an example format. It also explicitly positions itself as a detail-retrieval complement to search_awards, making it distinguishable from 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 when to use the tool: after obtaining a generated award ID from search_awards results. It does not explicitly name alternatives or state when not to use it, but the workflow context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: two are code/name resolvers, two are searchers (awards vs opportunities), one fetches award details, one looks up entities, and one aggregates spending. No two tools overlap in function, and descriptions reinforce the boundaries.
All tool names follow a consistent snake_case verb_noun pattern (find_agency, find_naics, get_award, lookup_entity, search_awards, search_opportunities, spending_by_category). The use of 'lookup' instead of 'find' is a minor stylistic variation but does not break the overall pattern.
Seven tools is well-scoped for a federal market intelligence server, covering the essential operations of entity resolution, searching, detail retrieval, and aggregation without unnecessary bloat. Each tool earns its place.
The tool set provides strong coverage of the federal market research workflow: resolving agencies and NAICS codes, searching opportunities and awards, retrieving award details, vetting entities, and aggregating spending. The only notable gap is the absence of a dedicated 'get_opportunity' tool to mirror 'get_award' for retrieving full opportunity details.