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Google Ads: rate a Local Services lead

lsa_rate_lead
Destructive

Rate one Local Services lead: survey_answer and a matching reason (none for NEUTRAL; OTHER needs other_reason_text). A rating is final; where Google offers lead credits (not EMEA, health care or tax) it returns a bonus-credit decision. Rate only from the customer's own words; never mark a real lead SPAM or DUPLICATE to get a credit.

Tools revision: 5c9bea8f.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNo
lead_idYes
customer_idYes
survey_answerYes
other_reason_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations declare destructive/non-idempotent, and the description goes further: "A rating is final" explains irreversibility, and it discloses the bonus-credit decision returned in eligible regions while excluding EMEA, health care and tax. That is real behavioral context beyond the structured hints.

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?

Three tight sentences, front-loaded with the action and the input pairing rule, followed by the irreversibility note and the safety constraint. The parenthetical region exclusions are dense but each sentence earns its place.

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?

Covers the mutation semantics, irreversibility, conditional parameter rules and the credit-decision outcome that the output schema returns. Minor gap: no statement about what happens if a lead was already rated or what errors look like, though "a rating is final" plus non-idempotent annotations implies the risk.

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 0%, so the description must compensate, and it does for the enum-driven fields (survey_answer, reason, other_reason_text) by stating the pairing rules. customer_id and lead_id are left uninterpreted, though their names are self-explanatory, so not a full 5.

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?

States a specific verb and resource ("Rate one Local Services lead") and immediately names the primary inputs, so the agent knows this writes a rating rather than reading leads, distinguishing it from lsa_leads. No ambiguity about what the tool does.

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?

Gives concrete conditional rules: a matching reason, none for NEUTRAL, other_reason_text when OTHER, plus a hard policy constraint ("Rate only from the customer's own words; never mark a real lead SPAM or DUPLICATE to get a credit"). It does not explicitly name a sibling or say when not to rate at all, which keeps it short of a 5.

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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