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AutomateLab-tech

Citation Intelligence MCP

signals_gsc_gap

Read-onlyIdempotent

Cross-reference Google Search Console rankings with AI citation checks to surface high-ranking queries that AI answers omit, revealing editorial gaps to target.

Instructions

Join Google Search Console performance with am_i_cited per query. Surfaces queries where the domain ranks well in Google but is not cited in AI - the closest editorial wins. Requires GCP service account creds (credentials_path or GOOGLE_APPLICATION_CREDENTIALS env).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to analyze, e.g. 'automatelab.tech'. Used both for the GSC site URL and the citation check.
engineNoAI engine for the citation check.auto
queriesYesQueries to cross-reference. 1-20 per call.
end_dateYesISO date for GSC range end, e.g. '2026-05-01'.
site_urlNoOverride the GSC siteUrl. Defaults to 'sc-domain:<domain>'.
start_dateYesISO date for GSC range start, e.g. '2026-04-01'.
credentials_pathNoPath to GCP service account JSON. Defaults to env GOOGLE_APPLICATION_CREDENTIALS.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesPer-query GSC + AI citation cross-reference.
rangeYesGSC date range.
domainYesDomain analyzed.
engineNoEngine used for the citation check.
site_urlYesGSC siteUrl used.
closest_winsYesQueries where domain ranks in Google top-10 but is not AI-cited (the editorial gap).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds a genuinely useful behavioral fact beyond that: it requires GCP service account credentials supplied via credentials_path or the GOOGLE_APPLICATION_CREDENTIALS env var, which affects whether the call can succeed.

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?

Three tight sentences with zero padding: purpose first, then the value proposition, then the credential prerequisite. Front-loaded and 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?

An output schema exists so return values need not be described, and all seven parameters are covered by the schema. The description supplies purpose, value and auth prerequisite, leaving only sibling routing (vs signals_bing_gap) 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 100%, so every parameter including the engine enum and date formats is already documented in the schema. The description only restates the credential source and the GSC/citation join, adding little semantics beyond what the schema provides. Baseline 3 is appropriate.

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 specific verb and resource: it joins GSC performance with citation data and surfaces queries where the domain ranks in Google but is not cited in AI. The phrase 'the closest editorial wins' adds a concrete framing of the output. It does not name the obvious sibling signals_bing_gap, so differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The value framing ('closest editorial wins') implies the scenario where this is useful, but there is no explicit when-to-use or when-not, and no reference to the near-twin signals_bing_gap. The usage context is implied rather than spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.