AutomateLab-tech/seo-performance-mcp
by Various
Post-publish SEO performance MCP. Unifies Google Search Console, Matomo, GA4, Clarity, and AI-citation signals per URL and emits a per-URL verdict.
MCP
AutomateLab-tech/seo-performance-mcp
Added 7 June 2026
Overview
Post-publish SEO performance MCP that unifies signals from Google Search Console, Matomo, GA4, Clarity, and AI-citation sources per URL. It processes these inputs and emits a single per-URL verdict. Written in TypeScript, it is designed for integration into AI agent workflows.
Best for
Best for
Developers building AI agents that need automated post-publish SEO insights
Use cases
- Monitor SEO performance after publishing across multiple analytics platforms
- Get a unified SEO verdict per URL from disparate data sources
- Integrate SEO signals into AI-driven content optimization pipelines
Notes
Post-publish SEO performance MCP that unifies signals from Google Search Console, Matomo, GA4, Clarity, and AI-citation sources per URL. It processes these inputs and emits a single per-URL verdict. Written in TypeScript, it is designed for integration into AI agent workflows.
0 stars on GitHub. Last updated 2026-05-31. Licensed MIT.
Use cases
- Monitor SEO performance after publishing across multiple analytics platforms
- Get a unified SEO verdict per URL from disparate data sources
- Integrate SEO signals into AI-driven content optimization pipelines
Pros
- Combines data from multiple analytics platforms into one verdict
- Provides a single per-URL SEO assessment
- Written in TypeScript for type safety and developer familiarity
Cons
- Zero stars indicates limited community adoption and validation
- Requires setup and maintenance of multiple analytics integrations
- Documentation and support may be sparse due to early stage
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Combines data from multiple analytics platforms into one verdict
- Provides a single per-URL SEO assessment
- Written in TypeScript for type safety and developer familiarity
Cons
- Zero stars indicates limited community adoption and validation
- Requires setup and maintenance of multiple analytics integrations
- Documentation and support may be sparse due to early stage
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