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FlorianBruniaux

gsc-mcp

by FlorianBruniaux
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Python
Remote

traffic_drops

Find Google Search Console queries whose clicks dropped versus the previous equal period; diagnoses ranking loss, CTR collapse, or demand decline.

Instructions

Find queries whose clicks dropped compared to the previous equally-sized period.

Each result includes a diagnosis: 'ranking_loss' (position degraded by more than 2), 'ctr_collapse' (CTR fell more than 30%), or 'demand_decline' (impressions also fell). Note: uses date.today() without a GSC reporting lag, so the most recent 2-3 days may be incomplete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
siteYes
engineNogoogle

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.0
    • addedInput schema / properties / engine
      Added value: +{
+  "default": "google",
+  "title": "Engine",
+  "type": "string"
+}
  2. First observedv0.4.1

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden and does substantial work: it defines the three diagnosis values and their exact thresholds ('position degraded by more than 2', 'CTR fell more than 30%', 'impressions also fell') and warns that recent 2-3 days may be incomplete due to the missing GSC reporting lag. It omits any auth or scope requirements, but the output semantics and data-freshness caveat are unusually informative.

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, front-loaded with the core purpose followed by output taxonomy and then the critical caveat. Every sentence adds distinct information with no filler.

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

Completeness3/5

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 explained, and the diagnosis taxonomy covers the interesting output semantics. However, with 0% parameter coverage and no annotations, the definition leaves the calling contract (what site/engine values are valid, how days scopes the comparison) under-specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and none of the three parameters (site, days, engine) are named or explained in the description. 'Equally-sized period' hints at how days is interpreted, but the agent gets no guidance on site formatting, engine options, or valid day ranges, so the description fails to compensate for the coverage gap.

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?

Names a specific verb and resource ('find queries whose clicks dropped') plus the exact comparison basis ('compared to the previous equally-sized period'), so the agent knows precisely what the tool computes. It does not differentiate itself from similar siblings like compare_search_periods, analytics_anomalies, or traffic_health_check, which keeps it out of the top band.

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 drop-detection framing implies the use case (diagnosing traffic declines per query), but there is no explicit when-to-use statement, no exclusion criteria, and no pointer to the competing period-comparison or anomaly tools in the sibling set. Usage is inferable but not guided.

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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