get_federal_debt_contextCurrent federal debt with context: total public debt outstanding, debt held by public vs intragovernmental holdings, and record date. Useful for fiscal policy discussions.
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the description adds value by specifying the exact data fields returned (total public debt outstanding, held by public vs intragovernmental, record date). This transparency goes beyond the annotations, though it doesn't discuss latency or frequency of updates.
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, front-loaded with the key function and data elements. Every word contributes to clarity, with no redundancy or filler.
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, no-parameter tool with annotations covering safety and open-world assumptions, the description provides sufficient context. It explains what the tool returns and its relevance. However, it lacks details on return format (e.g., JSON structure) or any caveats about data recency, which would be beneficial.
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?
There are no parameters, and schema coverage is 100% (no properties). With zero parameters, the baseline is 4. The description does not need to add parameter information, and it appropriately focuses on the output.
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 retrieves current federal debt with specific breakdowns (total, held by public vs intragovernmental, record date), which is distinct from siblings like get_federal_spending or get_federal_fiscal_data. The specific verb 'get' and resource 'federal debt context' with listed components leaves no ambiguity.
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 includes 'Useful for fiscal policy discussions,' which implies a context for use, but does not specify when not to use this tool or mention alternatives. It provides basic guidance but lacks explicit exclusion criteria or comparison to siblings.
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.