find_naicsResolve an industry description to NAICS codes (or look up what a NAICS code means). Use before search_opportunities/search_awards to get the right code. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Industry description ("cybersecurity services") or a NAICS code ("541512") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds the keyless nature and clarifies the input can be either a description or a code, which is useful beyond annotations. It does not contradict 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 entire description is three short sentences, front-loaded with the purpose, followed by usage context, and ending with 'Keyless.' Every sentence adds meaningful information with no fluff.
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 tool is simple with one parameter and no output schema. The description covers purpose, usage, and keyless access, giving the agent everything needed to select and invoke it correctly. It also contextualizes within the sibling toolset.
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 100%, and the query parameter description already explains the accepted inputs: 'Industry description ("cybersecurity services") or a NAICS code ("541512")'. The tool description repeats this without additional semantics, so baseline 3 applies.
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 uses specific verbs 'Resolve' and 'look up' with the resource 'NAICS codes', clearly stating the tool's dual functionality. It also distinguishes from siblings by positioning it as a preparatory step before search_opportunities/search_awards.
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 explicitly states when to use the tool: 'Use before search_opportunities/search_awards to get the right code.' This provides clear context and names the relevant sibling tools, giving the agent actionable guidance.
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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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.