analyze_industry_regulatory_landscapeFor a given industry sector: all regulatory actions (EPA, FDA, NHTSA), lobbying filings, campaign contributions, and committee jurisdiction. Maps sector to agencies and oversight committees.
| Name | Required | Description | Default |
|---|---|---|---|
| sector | Yes | Industry sector (e.g., Health, Energy, Finance, Defense, Transportation, Agribusiness) | |
| stateCode | No | Optional state filter for regulatory actions |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable context by listing the specific categories of data included (regulatory actions, lobbying, campaign contributions, committee jurisdiction) and the mapping function. This goes beyond what annotations provide, though it does not detail output format or potential rate limits.
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 consists of two short, clear sentences. The first sentence enumerates the data types covered, and the second explains the mapping function. There is no redundancy or extraneous information; every 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 tool has 2 parameters (one optional), no output schema, but annotations for safety, the description adequately explains what the tool returns (regulatory actions, lobbying, etc.) and its purpose. However, it does not specify the output format or structure, which could help the agent set expectations. Still, the core information is present.
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 both parameters having descriptions in the schema. The description mentions 'given industry sector' but does not add further meaning beyond the schema's examples (e.g., Health, Energy). The optional stateCode parameter is not elaborated in the description. Baseline 3 is appropriate as the schema already documents the parameters sufficiently.
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 analyzes the regulatory landscape for a given industry sector, listing specific data types (regulatory actions from EPA/FDA/NHTSA, lobbying, campaign contributions, committee jurisdiction) and noting it maps sector to agencies and oversight committees. This distinguishes it from sibling tools that focus on specific policy areas or single data sources.
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 obtaining a comprehensive regulatory overview of a sector, but it does not explicitly state when to use this tool versus more specialized sibling tools (e.g., analyze_energy_policy_influence) or provide any alternative suggestions. Usage context is present but no exclusions or guidance on alternatives.
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.