funding_signalsUS federal funding momentum for a technology keyword: USAspending obligations by fiscal year with growth, top recipients, NIH grant counts and recent projects. $0.02/call. Not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | ||
| years | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, open-world, and non-destructive behavior. The description adds useful context beyond those: it costs $0.02/call, is not investment advice, and uses USAspending data across fiscal years, which helps set expectations about dynamic data and paid usage. No contradiction with 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 two sentences with no filler. The core output features are front-loaded after the colon, and pricing plus disclaimer are appended compactly. Every sentence 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 two parameters and an output schema, the essential call shape is covered by schema plus description. However, the 'years' parameter semantics are under-specified, and there is no guidance on when to choose this tool over sibling tools, leaving some gaps for the agent to infer.
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%, so the description must compensate. It clarifies 'term' as a technology keyword and ties the data to fiscal years, but it never directly explains the 'years' parameter's range or effect beyond the schema's default of 5. This is partial but not complete compensation for a 2-parameter tool.
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 names a specific resource (USAspending) and enumerates concrete outputs: obligations by fiscal year, growth, top recipients, NIH grant counts, and recent projects. It makes clear this is a federal funding data lookup for a technology keyword, though it lacks an explicit verb like 'get' or 'retrieve'.
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 the intended use case ('for a technology keyword') but never states when to use this tool versus alternatives, names a sibling tool, or provides exclusion criteria. Sibling tools such as technology_momentum or technology_lookup may overlap, but no routing guidance is given.
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 are clearly separated by domain or action (credit, products, regulations, SEC, technology). The three technology tools could be confused at first glance, but their descriptions differentiate a ranked scan, a single-topic snapshot, and an on-demand deep analysis well.
Tool names follow a predictable lowercase snake_case pattern using noun phrases like credit_balance, funding_signals, and sec_events. The single deviation is list_products, which uses a verb_noun form, but the overall style remains consistent and readable.
Nine tools is well-scoped for a multi-domain intelligence API covering account balance, product catalog, funding, regulations, SEC data, and technology research. Each tool represents a distinct product offering without unnecessary sprawl.
The tool set covers the advertised product surface well, including account management, product discovery, and four data verticals. Minor gaps exist, such as no bulk funding search or regulatory detail drill-down beyond the provided signals, but agents can accomplish core workflows without dead ends.