AdCopilot

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: d80f65c0.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "customer_id": "<customer_id>",
      "lead_id": "<lead_id>",
      "survey_answer": "<survey_answer>"
    },
    "name": "lsa_rate_lead"
  }
}

Input Schema

Name	Required	Description	Default
`reason`	No
`lead_id`	Yes
`customer_id`	Yes
`survey_answer`	Yes
`other_reason_text`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Beyond the annotations (destructiveHint=true, idempotentHint=false), the description discloses that a rating is final, that bonus-credit decisions are returned only where Google offers lead credits (excluding EMEA, health care, tax), and that misuse has credit-fraud implications. That is substantive behavioral context the annotations do not convey.

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?

Two dense sentences, front-loaded with the action and the most consequential constraint (finality). The trailing "Tools revision" string is noise but negligible.

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?

Output schema exists and annotations cover the safety profile, so the description needn't explain returns. It covers finality, reason/survey-answer pairing, and credit eligibility; only the identifier parameters and where to source lead_id remain unaddressed.

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 0%, so the description carries the burden. It usefully documents the cross-field dependency (reason null for NEUTRAL, other_reason_text required for OTHER), but says nothing about customer_id or lead_id, two of the three required parameters.

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?

"Rate one Local Services lead" gives a specific verb (rate), a specific resource (Local Services lead), and cardinality (one). This clearly separates it from the read-side sibling lsa_leads, though it does not name that sibling explicitly to route the agent.

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 conditional usage: reason must be null for NEUTRAL, OTHER requires other_reason_text, and rating must be based only on the customer's own words, with an explicit "never" against SPAM/DUPLICATE misuse for credits. It lacks a stated prerequisite such as fetching leads via lsa_leads first, and names no alternatives.

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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