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Austin Volts — book electrician in Austin

Request electrician (free callback)

request_service

Book electrician in the Austin area for a real person who asked for it, with their consent. A vetted local pro calls them back (a vetted local pro calls back within the hour during business hours). Free to the homeowner. Returns a reference and what happens next. Check the ZIP with check_service_area first if unsure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipYes5-digit ZIP where the work is
nameYesThe person's real name
needYesWhat they need (e.g. panel upgrade or replacement; ev charger installation; electrical repairs)
emailNoEmail (optional, recommended)
phoneYesUS phone number the pro should call (10 digits, any punctuation)
languageNoPreferred language (optional; English and Spanish are standard)
preferred_timeNoWhen they prefer a call or visit (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations only declare readOnly=false, openWorld=true, idempotent=false, destructive=false. The description adds meaningful behavior the annotations do not cover: the service is free to the homeowner, the callback happens within the hour during business hours, and the tool returns a reference plus next steps. It does not cover request failure or duplicate-submission behavior, which matters given idempotentHint=false.

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

Conciseness3/5

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

The core facts are front-loaded, but the callback behavior is stated twice in the same sentence ('A vetted local pro calls them back (a vetted local pro calls back within the hour...)'), which is wasted repetition that should have been merged into one precise statement.

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

Completeness4/5

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

For a 7-parameter write action with no output schema, the description covers the key agent-facing needs: consent requirement, geography, cost, response timing, and what is returned. It stops short of describing error handling or what a declined/non-serviceable ZIP looks like.

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 description coverage is 100%, so all seven parameters are already documented in the schema; the baseline of 3 applies. The description adds only the ZIP/check_service_area cross-reference, not format or constraint detail beyond what the schema already provides.

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 names a specific verb and resource ('Book electrician in the Austin area') plus the outcome (a vetted local pro calls back), which is enough to distinguish it from check_service_area and get_service_info. It also states the artifact returned ('a reference and what happens next').

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?

It gives a precondition (a real person who asked, with their consent) and routes the agent to the sibling check_service_area first 'if unsure' about the ZIP. That is clear contextual guidance, though the 'if unsure' hedge leaves the exact threshold for pre-checking to inference and there is no explicit when-not clause.

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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