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SEO, AEO, GEO & Digital Marketing Audit + Safe Fix MCP Server

seo_audit_geo

Audit generative engine optimization by evaluating brand entities, Schema.org Organization/Person definitions, sameAs knowledge graph links, and author E-E-A-T signals to improve brand recognition in LLM-based search results.

Instructions

Audits Generative Engine Optimization (GEO): Evaluates brand and organization entities, Schema.org Organization/Person definitions, sameAs knowledge graph reconciliation (Wikidata, LinkedIn, Crunchbase), and Author E-E-A-T credentials.

USAGE GUIDELINES:

  • Use to evaluate how LLM-based search engines (ChatGPT Search, Claude, Gemini) comprehend brand identity and authority.

  • Do NOT use for local map pack NAP consistency; use 'seo_audit_local' instead.

  • Do NOT use for direct answer snippet definitions; use 'seo_audit_aeo' instead.

BEHAVIORAL TRANSPARENCY:

  • Safe, read-only diagnostic evaluation. No file modifications.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYesTarget live URL (e.g. "https://example.com") or local file path to audit.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.1
    • changedInput schema / properties / target / description
      Previous value: -"Target file path or live URL."New value: +"Target live URL (e.g. \"https://example.com\") or local file path to audit."
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It directly discloses safety-relevant behavior: 'Safe, read-only diagnostic evaluation. No file modifications.' This is meaningful beyond the schema and covers the most important behavioral trait for an audit tool. It could add more detail about return format or failure behavior, but the disclosed safety profile is strong.

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?

The description is well organized with clear sections: main purpose, usage guidelines, and behavioral transparency. It is front-loaded with the primary purpose, and every sentence earns its place. Despite covering multiple concepts, it remains tight and scannable.

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?

Given one required parameter with full schema coverage, no output schema, and no annotations, the description provides a complete picture: what the audit evaluates, when to use it, when not to use it, which sibling alternatives apply, and that it is read-only. Nothing essential for correct selection or invocation is missing.

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% and the single 'target' parameter is described in the schema as 'URL (e.g. "https://example.com") or local file path to audit.' The description does not add additional parameter-level semantics, but the schema already documents the parameter fully. Baseline 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 ('Audits') and resource ('Generative Engine Optimization (GEO)') and enumerates concrete evaluation areas: brand/organization entities, Schema.org definitions, sameAs knowledge graph reconciliation, and Author E-E-A-T credentials. It is clearly differentiated from siblings by naming what this tool addresses versus seo_audit_local and seo_audit_aeo.

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

Usage Guidelines5/5

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

The description explicitly says when to use the tool ('Use to evaluate how LLM-based search engines comprehend brand identity and authority') and provides two explicit exclusions with named alternatives ('Do NOT use for local map pack NAP consistency; use seo_audit_local instead' and 'Do NOT use for direct answer snippet definitions; use seo_audit_aeo instead'). This is exemplary routing guidance.

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