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 establish read-only and non-destructive behavior, and the description adds useful context beyond them: the $0.02/call cost, the 'not investment advice' caveat, and the specific data coverage from USAspending and NIH. This gives an agent a practical sense of the tool's scope and constraints.
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 compact and front-loads the core purpose, followed by high-signal details like price and disclaimer. The main sentence overflows with enumerated output components, but nothing is redundant or padded.
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 presence of an output schema helps cover return values, and annotations cover safety and world-openness. However, the undocumented 'years' parameter and the lack of sibling differentiation leave an agent with incomplete invocation context for a simple data-lookup tool.
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?
With 0% schema description coverage, the description carries the burden of explaining the parameters. It indirectly suggests 'term' is a technology keyword, but it never names the parameter or explains the 'years' parameter, its default, or its effect on the fiscal-year window. This is a meaningful gap for correct invocation.
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 identifies the tool as a federal funding signal lookup for a technology keyword and enumerates the concrete data points returned (USAspending obligations, growth, recipients, NIH grants, recent projects). It does not explicitly contrast itself against sibling tools like technology_momentum, but its resource focus is specific enough to be distinguishable.
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 tool should be used when an agent needs federal funding momentum for a technology topic, but it does not explicitly state when to prefer it over alternatives such as technology_momentum or technology_emerging. There is no exclusionary guidance or sibling comparison.
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 targets a distinct data domain: billing/product info, federal funding, federal regulations, SEC filings/events, and research topic analytics. The three technology_* tools are related but differentiated by scope (ranked list, snapshot, deep analysis), so an agent should be able to select correctly with descriptions. Minor potential confusion exists between funding_signals and sec_events since both emit 'signals', but they cover clearly different sources.
All names are snake_case and map to clear domains, but the pattern is not uniform: list_products uses verb_noun, while funding_signals, reg_rules, sec_company, and technology_lookup use noun-style names. The sec_* and technology_* prefixes help navigation, but there is no consistent verb convention across the set.
Nine tools is a well-scoped size for a data-as-a-service server. It includes meta tools (credit, product list) and seven data products without obvious redundancy. Each tool appears to earn its place.
As a read-only data API, full CRUD is not expected, and the server covers billing, product selection, federal money, regulations, SEC events/company snapshots, and emerging tech analytics. The main gap is a lack of a general search/discovery tool, but the specialized endpoints cover their stated purposes well.