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mcp-seo-audit

mcp-seo-audit

by GiorgiKemo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

compare_seo_audits

Read-onlyIdempotent

Identify new, resolved, and persistent SEO issues by comparing two audit reports. Track changes without re-crawling URLs.

Instructions

Compare two JSON outputs of get_seo_audit_report with matching crawl settings. Classifies new, resolved, persistent, newly observed and unverified issues without fetching URLs or writing files. Missing/failed pages never prove an issue resolved.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "baseline_json": "<baseline_json>",
      "current_json": "<current_json>"
    },
    "name": "compare_seo_audits"
  }
}

Input Schema

NameRequiredDescriptionDefault
current_jsonYes
baseline_jsonYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the description is not carrying the safety burden. It still adds real value beyond annotations: it enumerates the classification outcomes and discloses two behavioral traits (no URL fetching, no file writes) plus the important caveat that missing/failed pages never prove resolution.

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. The core purpose and input source lead, followed by classification behavior and the key caveat, all front-loaded and each earning its place.

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

Completeness5/5

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

With an output schema present, return-value explanation is not required. The description covers input provenance, the no-fetch/no-write constraint, and the resolution caveat, leaving nothing an agent needs to call it 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?

Schema description coverage is 0%, so the description must compensate. It explains that both inputs are JSON outputs of get_seo_audit_report, which maps the two parameters to their expected content, but it does not explicitly clarify the baseline-vs-current ordering or format expectations beyond that.

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?

States a specific verb (Compare) and precise resources (two JSON outputs of get_seo_audit_report), and explicitly names the sibling tool that produces the inputs. An agent can distinguish it from related tools like compare_project_audits or get_seo_audit_report without opening a schema.

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?

Ties use to a concrete source (outputs of get_seo_audit_report) and states the prerequisite that crawl settings must match. There is no explicit when-not or alternative routing, but the input provenance and precondition give clear context for when the tool applies.

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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