get_opportunity_extractedGet exact citations, requirements, costs, dates, forms, and submission text extracted without an LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| notice_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by stating the output is 'exact' and 'without an LLM', indicating a deterministic and reproducible extraction process. This goes beyond annotations and does not contradict them.
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, front-loaded sentence: 'Get exact citations, requirements, costs, dates, forms, and submission text extracted without an LLM.' It contains no filler, immediately states the action, and lists the returned content types concisely. Every word 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?
For a simple read-only tool with one parameter and strong annotations, the description is mostly complete. It lists the kinds of extracted data (citations, requirements, costs, dates, forms, submission text), which gives a good sense of the output. However, there is no output schema, and the description does not mention behavior when data is not found or how the data is structured, leaving minor gaps. Given the simplicity, 4 is appropriate.
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 one parameter, notice_id, with no description, resulting in 0% schema coverage. The tool description does not mention the parameter, but the name 'notice_id' is self-explanatory and the tool name 'get_opportunity_extracted' clarifies it refers to an opportunity notice. Since the parameter is trivial, a score of 3 is defensible, though better compensation in the description would be ideal.
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 returns 'exact citations, requirements, costs, dates, forms, and submission text extracted without an LLM.' This identifies a specific verb+resource and distinguishes it from sibling tools like get_opportunity by emphasizing deterministic (non-LLM) extraction, making the purpose unmistakable.
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 phrase 'without an LLM' implies this tool is for deterministic facts rather than AI-generated summaries, but it does not explicitly name alternatives or provide when-not-to-use guidance. Sibling tools like get_opportunity and search_extracted_facts exist, yet no direct comparison or exclusion is given, leaving usage implied rather than explicit.
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.