get_climate_dataNOAA climate normals for a state: average temperature, min/max temperatures, precipitation, and snowfall from 30-year normal period. Requires NOAA_TOKEN.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g., CO) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds requirement for NOAA_TOKEN beyond annotations (readOnlyHint, openWorldHint). Discloses data source (NOAA) and period (30-year normals). No contradiction with 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?
Two sentences, first defines purpose and data, second specifies authentication. Efficient and front-loaded, though could be more structured.
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 1-parameter tool with schema coverage 100% and no output schema, description adequately specifies input, data returned, and auth requirement. Missing details on output format or pagination, but sufficient for basic use.
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 parameter description 'Two-letter state code (e.g., CO)'. Description adds no new semantic meaning beyond schema, earning baseline score.
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?
Description clearly states verb 'get' and resource 'NOAA climate normals', listing specific data fields (average temperature, min/max, precipitation, snowfall) and period (30-year normal). Differentiates from siblings by specifying NOAA source and data types.
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?
No guidance on when to use this tool versus alternatives like get_state_climate_profile. Does not mention prerequisites beyond auth token, nor when not to use.
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.