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District cross-domain profile

analyze_district_comprehensive
Read-only

District representative info combined with STATE-level context across domains: environment (EPA), safety (FEMA, CFPB), health (CMS), economy (EIA, education, research, banking). Domain counts are statewide aggregates — these sources do not publish district-level rollups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateCodeYesTwo-letter state code (e.g., PA)
districtNumberYesDistrict number (0 for at-large)

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds important behavioral context: data is statewide aggregates, not district-level, and lists specific sources (EPA, FEMA, etc.). 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is two sentences, front-loaded with the purpose. Every sentence adds value, but could be slightly more structured (e.g., listing domains more explicitly). No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should clarify return format. It names domains but lacks detail on output structure (e.g., is it a JSON object with keys per domain?). The context that data is statewide aggregates is helpful, but for a comprehensive tool, more specifics would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema provides 100% coverage for the two parameters (stateCode and districtNumber). Description does not add additional semantics beyond what the schema already includes (e.g., districtNumber 0 for at-large is in schema). Baseline 3 is appropriate since schema fully documents parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool combines district representative info with statewide context across multiple domains (environment, safety, health, economy). This distinguishes it from sibling tools that focus on single domains (e.g., get_district_environmental_profile) or specific representatives (get_representative_profile).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies that domain counts are statewide aggregates and these sources do not publish district-level rollups, which informs when to use this tool (broad overview) vs. sibling district-specific tools. It does not explicitly state when not to use it or name alternatives, but the context provided is clear.

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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TDQS

A3.9/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count3/5

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.

Completeness5/5

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.