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Suganthan-Mohanadasan

BigQuery MCP Server

gsc_alerts

Detect SEO issues like position drops, CTR declines, and click losses. Get severity-rated alerts to prioritize fixes.

Instructions

Check for SEO alerts: position drops, CTR collapses, click losses, and pages that disappeared from search results. Returns severity-rated alerts so you know what needs attention first. IMPORTANT: Base your analysis ONLY on the data returned. Report exact numbers from the results. Do not speculate about causes (e.g. algorithm updates, competitor actions) unless the data explicitly supports it. If the data does not contain enough information to answer a question, say so clearly rather than guessing. PRESENTATION: Always present these results as a rich, interactive visualization using artifacts. Use summary cards for key metrics, colour-coded indicators for severity or change direction (green for positive, red for negative), bar charts or sparklines for comparisons, and tabbed sections to organise different categories of results. Make the output visually scannable and dashboard-like rather than plain text or raw tables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days per period to compare
datasetNoBigQuery dataset containing GSC data
ctr_drop_pctNoAlert if CTR drops more than this percentage
click_drop_pctNoAlert if clicks drop more than this percentage
position_drop_thresholdNoAlert if position drops more than this many spots
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It clearly says the tool checks for and returns severity-rated alerts, and adds critical behavioral rules (only report data, don't speculate, present visually), which is useful. However, it does not address operational details such as permissions, rate limits, or whether the operation is read-only, so transparency is partial.

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 uses clear sections. The PRESENTATION block is lengthy and includes agent-facing style instructions that could be viewed as beyond the tool's core description, but every section serves a distinct purpose, so it remains appropriately structured.

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?

Given the lack of output schema and annotations, the description provides a good level of context: alert categories, severity ratings, how to handle data gaps, and presentation expectations. It does not specify exact response fields, but for an alert tool this is reasonably complete.

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 coverage is 100%, so the schema already documents all five parameters with descriptions. The tool description adds context about alert types and severity but does not add new parameter-specific meaning; the 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?

The description uses a specific verb 'Check' with a clear resource ('SEO alerts') and enumerates concrete alert types (position drops, CTR collapses, click losses, disappeared pages), making its function clear. However, it does not explicitly distinguish itself from sibling tools like gsc_traffic_drops or gsc_anomalies, so it stops short of full sibling differentiation.

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 description implies when to use the tool—when the user needs a prioritized list of SEO alerts—but it does not state explicit alternatives or exclusions. No guidance on choosing this over gsc_traffic_drops or gsc_anomalies is provided; the 'when to use' is inferred from the purpose rather than stated.

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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