get_federal_fiscal_dataFederal fiscal overview from Treasury: national debt, monthly revenue by category, and spending by category. Returns current figures and fiscal year totals.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Fiscal year for revenue/spending (default: current year) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and variability. The description adds that it returns current figures and fiscal year totals, but does not elaborate on data freshness, constraints, or potential delays. It adds marginal value beyond the 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 a single sentence of 18 words, immediately stating the source and scope. It is front-loaded with 'Federal fiscal overview from Treasury' and conveys all key elements without waste.
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?
Given the tool has one optional parameter, no output schema, and annotations providing safety and variability hints, the description covers the returned data types (national debt, revenue, spending) adequately. However, it lacks specifics on output format or detailed categories, and could improve with a note on how the year parameter affects results.
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% with the 'year' parameter already described in the schema. The description does not add extra meaning about the parameter beyond what the schema provides, so it meets the baseline for a tool with high schema coverage.
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 provides a federal fiscal overview from the Treasury, covering national debt, monthly revenue by category, and spending by category, with current figures and fiscal year totals. This differentiates it from sibling tools like get_federal_debt_context (focused on debt) and get_federal_spending (focused on spending), making the purpose distinct.
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 is for a broad fiscal overview but does not explicitly state when to use it versus alternatives like get_federal_spending or get_federal_debt_context. No guidance on when not to use it or prerequisites, leaving inference to the agent.
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 clear, specific purpose with detailed descriptions that differentiate them. Prefix patterns like get_district_, search_, analyze_, get_, etc., help an agent easily identify the correct tool for a task.
All tool names use a consistent verb_noun or verb_noun_noun pattern with underscores. The naming convention is uniform across the entire set, with no mixing of styles or ambiguous verbs.
With 47 tools, the count is high but justified by the broad scope of civic data analysis. While some agents might find the sheer number overwhelming, the tools are organized into clear categories (district profiles, searches, analyses) that make navigation feasible.
The toolset covers an impressively wide range of domains: legislation, representatives, districts, voting, committees, campaign finance, lobbying, federal spending, regulations, environment, energy, healthcare, housing, disaster, banking, consumer complaints, crime, vehicles, and more. There are no obvious missing operations for a civic data platform.