"A guide to performing an SEO audit" matching MCP servers:

MCP server that enables AI coding assistants to audit repositories against engineering rules, generating self-contained HTML reports with citations and optional GitHub issue filing.

An advanced, AI-powered Model Context Protocol (MCP) server that acts as a comprehensive Growth Auditor, SEO Optimizer, and Code Fixer.

Provides access to clean code guide documents and enables code quality inspection of external project files based on coding standards and best practices.

MCP server for Qi-An Xin CodeSafe, enabling retrieval of scan tasks, defect details, source code context, and taint propagation paths, with secure write-back of audit conclusions.

Enables MCP-compatible agents to audit an entire repository through one question, detecting stacks and running parallel checks for dead code, dependencies, security, CVEs, complexity, and lint across Laravel/PHP, Python, TypeScript/React, and Flutter/Dart. It returns normalized, severity-sorted findings with false positives flagged and secret values kept out of the output.

Enables an AI agent to statically triage local binaries and APKs by extracting and classifying strings, flagging hardcoded secrets, detecting file types, checking exploit-mitigation flags, and auditing Android manifests. Results from all tools can be merged into a single structured report with a summary findings list.

An AI-native code security audit MCP server that supports multi-language AST analysis, call graph analysis, vulnerability detection, and AI deep audit.

Enables users to audit Dockerfiles for container-security anti-patterns, including missing USER, baked-in secrets, curl | sh / wget | bash, unpinned base images, and ADD with remote URLs. It parses Dockerfile structure to return risk levels and actionable fixes.

Enables coding agents to audit a Next.js codebase read-only, checking routes, Prisma models, environment variables, and locale consistency via MCP tools.

A multi-agent system that scans a JavaScript or TypeScript source file for unbalanced addEventListener calls, flags memory-leak vectors, and produces a validated patch via a Gemini LLM.

Enables AI coding agents such as Cursor, Claude, and Windsurf to run local SEO audits, inspect routes, apply safe code fixes, and access Google Search Console, Bing, and Core Web Vitals data through 13 standard tools.

Enables scanning of code and URLs for security vulnerabilities (hardcoded secrets, missing headers, etc.) via Claude Code, integrating with the vibe-audit security scanner.

Static audit of Kubernetes manifests via MCP, powered by kube-linter. Returns structured reports with severity, check ID, category, message, and remediation hints.

An MCP server that audits Dockerfiles for security and quality issues, providing structured reports with severity, line numbers, remediation, and fix snippets.

Lets developers and MCP agents run deterministic local HTML audits, turning inline HTML into structured findings for missing labels, unnamed buttons, skipped headings, ambiguous links, duplicate IDs, and missing landmarks. It also exposes a rule list and returns stable, actionable reports without external API keys.

MCP server that audits npm dependencies against the live registry, providing per-dependency reports on versions behind, deprecation, and license.

Enables auditing a repository's Claude Code setup, scoring it from 0 to 100, and scaffolding agents and hooks to fix issues.

Enables an AI agent to assess Magento 2 / Adobe Commerce projects for target PHP and Magento version compatibility, custom module quality, and dependency security, combining results into concrete upgrade blockers and plans. It provides tools for project status, package update verdicts, module risk scans, release notes, and upgrade planning.

A local MCP server that audits software projects against the UK ICO Age Appropriate Design Code (Children's Code) entirely on the user's machine.

Enables AI coding agents to perform evidence-based, read-only audits of supported software projects by exposing bounded project inventory and static-analysis findings for discovery, code review, integration tracing, security, and tests. Reports are returned as Markdown or JSON directly in the tool response, with no code executed or files written.

Enables read-only, evidence-led security audits of GitHub-hosted source code through explicit workflow primitives: repository reconnaissance, coverage-ledger planning, heuristic candidate leads, structured findings validation, independent verification gates, and JSON/Markdown report generation. It never clones, executes, or probes the target code, and marks runs incomplete until coverage and reviewer-verification gates pass.

Enables users to statically analyze regular expressions for catastrophic-backtracking (ReDoS) risk through real AST parsing rather than executing the pattern, flagging nested quantifiers, ambiguous alternation, and backreferences. It also generates candidate proof-of-concept attack strings with a timeout-guarded test snippet and suggests JavaScript-safe rewrites.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables users to scan AI agent skills and configuration files for hidden Unicode instructions, prompt injection, download-and-execute payloads, exfiltration patterns, and overly broad permissions before installation. It provides static audit tools for skill files, agent configs, and revealing hidden text without executing or fetching the input.

Enables scanning arbitrary text and added lines in unified git diffs for accidentally committed credentials, using precise vendor-format matches and entropy-based detection. All detected secrets are redacted before results are returned, helping coding agents and developers catch leaks without exposing full values.

Enables security scanning of code projects to identify common vulnerabilities like XSS, injections, SSRF, and path traversal issues. Provides local, offline scanning with severity-grouped results and actionable fix suggestions for improving code security.

Enables automated security code auditing using LLM and MCP, including AST parsing, taint analysis, dataflow tracing, and automated PoC generation for multi-language codebases.

Enables static auditing of Pine Script v6 strategies to catch backtest-vs-live divergence traps before deployment, returning specific lines and plain-English explanations. It identifies fill-timing, ATR-smoothing, lookahead bias, VWAP-anchoring drift, exit-drift, and v6 syntax issues.

Scores MCP tool definitions and tool lists from 0-100, flagging vague descriptions, missing parameter docs, non-verb-first naming, oversized enums, and nested schemas with severity, rationale, and concrete fixes. It also validates marketplace listings against platform character caps such as MCPize's 500-character short-description limit before deploy.

Detects leaked credentials in source code with tools to scan text, files, and directories for API keys, tokens, and private keys across 30+ providers.