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vdalhambra/siteaudit-mcp

by Various

Instant SEO, performance, and security audits for any URL — an MCP server for AI agents

V

MCP

vdalhambra/siteaudit-mcp

Added 1 June 2026

#accessibility #ai-tools #anthropic #claude #fastmcp #mcp #model-context-protocol #python

Overview

An MCP server for AI agents that performs SEO, performance, and security audits for any URL. It is written in Python and enables automated site assessments through the Model Context Protocol.

Best for

Best for
AI agent developers needing quick, combined site audits without managing multiple APIs

Use cases

  • Automating SEO audits for client websites via AI agents
  • Checking site performance metrics during development workflows
  • Assessing security vulnerabilities in web pages programmatically

Notes

An MCP server for AI agents that performs SEO, performance, and security audits for any URL. It is written in Python and enables automated site assessments through the Model Context Protocol.

3 stars on GitHub. Last updated 2026-04-17. Licensed MIT.

Use cases

  • Automating SEO audits for client websites via AI agents
  • Checking site performance metrics during development workflows
  • Assessing security vulnerabilities in web pages programmatically

Pros

  • Provides instant, multi-aspect audits (SEO, performance, security) in a single tool
  • Integrates with AI agents through the MCP protocol for automated pipelines
  • Lightweight Python implementation easy to set up as a server

Cons

  • Small community and limited GitHub stars (3), indicating low adoption
  • Audit depth may be basic compared to dedicated standalone tools
  • Requires an MCP-compatible AI agent to function, adding dependency

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides instant, multi-aspect audits (SEO, performance, security) in a single tool
  • Integrates with AI agents through the MCP protocol for automated pipelines
  • Lightweight Python implementation easy to set up as a server

Cons

  • Small community and limited GitHub stars (3), indicating low adoption
  • Audit depth may be basic compared to dedicated standalone tools
  • Requires an MCP-compatible AI agent to function, adding dependency