get_award_evidenceSeparate an official notice awardee, linked prior award recipients, and market participants without claiming an incumbent.
| Name | Required | Description | Default |
|---|---|---|---|
| notice_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, open-world, and non-destructive. The description adds a useful nuance with 'without claiming an incumbent,' but does not disclose output shape, pagination, or authorization expectations.
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 compact sentence with no filler words. It loses one point because the phrasing is awkward and sacrifices clarity for brevity.
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?
This is a simple one-parameter read-only tool with strong annotations, so the baseline context is present. Still, because there is no output schema, the description should have explicitly indicated what the tool returns or how the 'separation' is represented.
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 0%, and the description never mentions notice_id or explains how it should be used. The parameter name is self-explanatory, but with no description-level compensation the semantics remain minimally covered.
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 title identifies the tool as getting opportunity award evidence, and the description names concrete entities: official notice awardee, linked prior award recipients, and market participants. However, the verb 'Separate' is ambiguous about whether the tool returns categorized data or performs some internal distinction, making the purpose less clear than it could be.
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 offers no guidance about when to use this tool instead of related siblings such as get_award, get_opportunity, or get_vendor. There are no stated alternatives, prerequisites, or exclusion conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clear boundaries (e.g., get_award vs search_awards, get_business_profile vs get_vendor). Some overlap exists in pairs like find_recompetes/find_recompetes_for_business and search_opportunities/search_opportunities_for_business, though the latter are differentiated by profile context.
All tools follow a consistent verb_noun snake_case pattern (get_, search_, update_, watch_, save_, etc.) with no stylistic deviations. Naming is highly predictable across the entire set.
At 39 tools, the surface is excessively large. While the domain is broad, many tools could be combined or parameterized (e.g., get_business_awards and get_vendor_awards, multiple search variants). This creates overhead for agents and dilutes focus.
The tool set covers the core lifecycle: opportunity/award searching and retrieval, business profile updates, saved searches, watches, exclusions, and reference data. Minor gaps such as award amendments or more granular exclusion handling exist, but the surface is fundamentally complete.