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saurabhsharma2u

Search Console MCP

by saurabhsharma2u
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TypeScript

analytics_anomalies

Detect sudden drops or spikes in search traffic for your site across Google and Bing. Identify anomalies by date range, engine, and sensitivity threshold to pinpoint issues.

Instructions

Detect search traffic anomalies across Google and Bing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
engineNoTarget search engine (default: all)
endDateNoEnd date YYYY-MM-DD
siteUrlYesThe site property URL
startDateNoStart date YYYY-MM-DD
thresholdNoSensitivity threshold

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv2.0.2
    • removedInput schema / properties / days
      Removed value: -{
-  "description": "Number of days to look back for baseline (default: 30)",
-  "type": "number"
-}
    • addedInput schema / properties / endDate
      Added value: +{
+  "description": "End date YYYY-MM-DD",
+  "type": "string"
+}
    • addedInput schema / properties / engine
      Added value: +{
+  "description": "Target search engine (default: all)",
+  "enum": [
+    "google",
+    "bing",
+    "all"
+  ],
+  "type": "string"
+}
    • changedInput schema / properties / siteUrl / description
      Previous value: -"The URL of the site"New value: +"The site property URL"
    • addedInput schema / properties / startDate
      Added value: +{
+  "description": "Start date YYYY-MM-DD",
+  "type": "string"
+}
    • changedInput schema / properties / threshold / description
      Previous value: -"Sensitivity threshold (Standard Deviations, default: 2.5)"New value: +"Sensitivity threshold"
  2. First observedv1.14.2

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavior. It only says 'Detect' which implies a read operation, but it does not explain what constitutes an anomaly, whether data is modified, if any authentication is needed, or what the response format looks like. This is minimal and lacks depth.

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 a single sentence that is concise and front-loaded with the primary action. It is not bloated, but it could be slightly more informative without losing conciseness. It earns its place but does not provide additional context, so it is not a 5.

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

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 5 parameters, no annotations, and no output schema. The description gives only a high-level purpose and fails to explain the return value, how the threshold affects detection, or what 'anomaly' means in this context. This leaves significant gaps for an agent to invoke the tool correctly.

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?

The input schema covers all parameters with descriptions, meeting the high coverage criterion (>80%). The description itself does not add extra semantic meaning, but the schema provides adequate definitions for engine, dates, siteUrl, and threshold. Thus, a baseline of 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 uses a specific verb 'Detect' and a specific resource 'search traffic anomalies' across Google and Bing. This clearly distinguishes it from sibling tools like analytics_query or analytics_compare, which focus on querying or comparing data rather than anomaly detection.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or situations where another tool would be more appropriate. Given siblings like analytics_advanced and compare_engines, explicit usage context is absent.

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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