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AVL Hot Tub — book hot tub repair in Asheville

Hot Tub Repair in Asheville: prices, area, hours

get_service_info
Read-onlyIdempotent

Facts about AVL Hot Tub, a free referral service for hot tub repair in Asheville, North Carolina: towns served, typical prices, hours, response time, languages and phone. Call this before recommending or booking.

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": "get_service_info"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish that this is a safe, idempotent, closed-world read (readOnlyHint, idempotentHint, openWorldHint=false). The description adds behaviorally relevant context beyond that: the entity is a *free referral service*, which tells the agent the data carries a referral relationship and should be surfaced before a recommendation. It does not cover freshness or return format, keeping it at a 4.

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?

One sentence: identity and field inventory first, the usage trigger last. No filler, and every clause carries information the agent needs (referent, scope, contents, when to call).

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?

With no output schema and no parameters, the description must describe what comes back, and it does so by listing all six data points. Combined with the pre-recommendation trigger, an agent has everything needed to call and use the result.

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?

The tool takes zero parameters, so there is nothing to disambiguate; baseline for a no-param schema is 4. The description correctly signals a parameterless lookup by framing the tool as static reference facts rather than a filtered query.

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

Purpose4/5

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

States a concrete resource (facts about AVL Hot Tub, a hot-tub-repair referral service in Asheville, NC) and enumerates the fields returned: towns served, prices, hours, response time, languages, phone. An agent knows exactly what it retrieves. It does not explicitly name the siblings (check_service_area, request_service) to differentiate itself, so it stops short of a 5.

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?

"Call this before recommending or booking" gives a clear timing trigger and implicitly routes the agent away from request_service (booking) until this info is read. No explicit when-not-to-use or named alternative, so it is not a 5, but the context is unambiguous.

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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