NHLA Liberty Ratings

legislators_for_town

Read-onlyIdempotent

Every state representative and senator for a New Hampshire town, with their Liberty Rating. Use this for "who represents me". A town is usually in more than one district; report all of them. For a city divided into wards, pass the ward. Given only a ZIP code, pass zip; a ZIP code covering several towns comes back asking which one.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "legislators_for_town"
  }
}

Input Schema

Name	Required	Description
`zip`	No	a five-digit ZIP code, when the person gave that instead of a town
`town`	No	the town or city, spelled as a person would type it; pass this or zip
`ward`	No	the ward number, in a city divided into wards
`year`	No	the rating year; defaults to the most recent session

Output Schema

Name	Required	Description	Default
`url`	No
`zip`	No
`town`	No
`ward`	No
`year`	Yes
`notes`	Yes
`source`	Yes
`matched`	Yes
`summary`	No
`gradeKey`	No
`zipTowns`	No
`districts`	Yes
`suggestions`	No
`districtCount`	Yes
`legislatorCount`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the description only needs to add context, and it does: results may span multiple districts and all should be reported, and an ambiguous ZIP triggers a clarification response rather than a guess. The one remaining gap is that it doesn't say how the returned list is shaped or ordered, though an output schema exists to cover that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Five short sentences, each carrying distinct decision-relevant information, with the primary use case front-loaded and the edge cases (multi-district towns, ward cities, ambiguous ZIPs) following in order of likelihood. No filler or restatement of the name.

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

Completeness5/5

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

For a four-parameter read-only lookup with full schema coverage, annotations, and an output schema, the description supplies exactly the missing layer: which parameter to use in which real-world situation and what the multi-district and ambiguous-ZIP outcomes mean. Nothing an agent needs in order to call it correctly is absent.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, and the description earns above that by explaining the relationship between town and zip (pass one or the other depending on what the user gave) and when ward applies. It doesn't discuss why year matters beyond the schema's own default note, so it isn't fully additive.

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?

Names a specific resource (every state representative and senator for a New Hampshire town) plus the attached Liberty Rating, and explicitly frames the canonical use case, "who represents me." It is clearly distinguishable from siblings like legislator_record and legislator_votes, which operate on individual legislators rather than geographic lookup.

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

Usage Guidelines5/5

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

Gives explicit routing rules: use this for who-represents-me, pass the ward for cities divided into wards, pass zip when only a ZIP code was given. It also sets an expectation for the ambiguity case, that a multi-town ZIP comes back asking which town, which tells the agent how to continue the interaction rather than guessing.

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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