analyze_policy_area_ecosystemFor a Congress.gov policy area: related agencies, industry sectors, lobbying activity, committee oversight, and Federal Register keywords. Uses policy-area-map as the cross-domain join hub.
| Name | Required | Description | Default |
|---|---|---|---|
| policyArea | Yes | Congress.gov policy area (e.g., "Health", "Energy", "Finance and Financial Sector") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint. The description adds behavioral context by revealing that the tool uses a 'policy-area-map' as a cross-domain join hub, and lists the types of related entities returned. No contradictions.
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?
Two sentences, no wasted words. Front-loaded with the core purpose, then additional structure detail. Each sentence adds value.
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 simple parameter and no output schema, the description sufficiently explains what the tool returns and how it works. Annotations provide safety cues. Complete for its scope.
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 is only one parameter, fully documented in the schema with examples. The description does not add additional meaning beyond the schema, but the schema coverage is 100%, so baseline 3 is appropriate.
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's purpose: analyzing a Congress.gov policy area to find related agencies, industry sectors, lobbying activity, committee oversight, and Federal Register keywords. It distinguishes from sibling tools by being generic, while siblings focus on specific domains like consumer protection or energy.
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 use when exploring a policy area's ecosystem, and the sibling list shows many domain-specific tools, indicating this is the general option. However, it doesn't explicitly state when not to use it or provide alternatives, but the context is clear.
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 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.