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justingluska

Google Search Console MCP Server

by justingluska

Verify Claim

verify_claim
Read-only

Re-queries Google Search Console API to confirm a specific metric matches an expected value, ensuring claims about search performance are accurate before presenting them.

Instructions

Self-check a claim about search performance data before presenting it. Re-queries the Google Search Console API to verify that a specific metric matches the expected value. Use this tool to confirm numbers before stating them in your analysis, especially for claims from earlier in the conversation. IMPORTANT: Base your analysis only on the data returned by this tool. Report exact numbers from the response. Do not speculate about causes the data does not support. If the data is insufficient to answer, say so rather than guessing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesThe claim to verify (e.g., "homepage gets 500 clicks per week")
metricYesThe metric to check
endDateNoEnd date in YYYY-MM-DD format
siteUrlNoThe site URL. Falls back to GSC_DEFAULT_SITE_URL if not provided.
startDateNoStart date in YYYY-MM-DD format
toleranceNoAcceptable difference as a percentage (default: 5). A value of 5 means the actual value can differ by up to 5% from the expected value.
pageFilterNoFilter to a specific page URL
queryFilterNoFilter to a specific query
expectedValueYesThe expected value of the metric

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish this is read-only and non-destructive. The description adds valuable behavioral guardrails: base analysis only on returned data, report exact numbers, avoid speculation, and state when data is insufficient. This goes beyond the structured annotations without contradicting them.

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?

The description is front-loaded with the core purpose and then adds concise behavioral guidance. The IMPORTANT section has some redundancy among its prohibitions, but each sentence still contributes to correct usage.

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 tool with no output schema, the description compensates by telling the agent to rely only on returned data and to report exact numbers. It does not specify the response shape, but the verification context and parameters are covered well enough for correct invocation.

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 the schema already documents all 9 parameters. The description adds little parameter-specific meaning beyond reinforcing that a metric is checked against an expected value, which the schema also conveys. Baseline 3 is appropriate.

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 states a specific verb and resource: re-querying the Google Search Console API to verify a claim about search performance data. It clearly differentiates this tool from analysis/reporting siblings like search_analytics and compare_periods by framing it as a verification step before presenting numbers.

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 gives clear context for when to use the tool: before presenting claims, especially ones from earlier in the conversation. It does not explicitly name alternatives or state when not to use it, so it stops 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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