"DevOps tools and practices including CI/CD pipelines, GitOps, and DevSecOps" matching MCP servers:

Enables read-only inspection of Git repository status and diff summaries, along with validation of OpenTofu and Ansible configurations, through a strictly limited, non-mutating toolset.

Enables MCP clients to run deterministic, read-only local repository audits for risky GitHub workflows, undocumented environment variables, possible credentials, and conflicting dependency configuration, returning structured findings without sending source code to a service.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Provides AI-callable tools for modernizing legacy applications, including stack detection, API contract extraction, SQL to Flyway conversion, and migration effort estimation.

Provides documentation generation and analysis tools for AI agents, including JSDoc generation, README analysis, changelog generation, API documentation, and code comment analysis.

Local MCP servers that give Claude Code access to other tools mid-session.

Enables AI coding assistants to run static analysis on GitHub repositories, raw code snippets, or local directories to detect cloud cost leaks in Terraform/AWS configs, PostgreSQL migration table-lock hazards, and OWASP/Next.js API security gaps. Each finding returns structured severity, line-level detail, and drop-in fixes.

Provides Python and FastAPI coding best practices with searchable guidelines, code review capabilities, and actionable improvement suggestions with examples across general coding, performance, and code quality categories.

Exposes a Ci-mvp security-triage pipeline as chat tools for Claude Desktop, enabling on-demand queries of Trivy/Semgrep scan results stored in S3.

Unified MCP server: hybrid search + code graph + semantic memory. 10 tools, <350MB RAM, fully local. No API keys.

Enables AI agents to safely read, search, and inspect a local git repository, including PR-style diffs and changed-file summaries. It also supports opt-in allowlisted test runs.

MCP server providing npm registry search, package details, dependency auditing, bundle size estimation, and package comparison tools for AI agents.

A Model Context Protocol server that provides tools to find regex pattern positions in files and list allowed directories, enabling text analysis with LSP-like functionality.

Provides AI assistants with frontend development tools including component scaffolding, bundle analysis, accessibility checks, and responsive design guidance. Enables automated generation of React components with tests and stories, bundle optimization recommendations, and WCAG compliance fixes.

A Model Context Protocol (MCP) server designed to easily dump your codebase context into Large Language Models (LLMs).

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

MCP server providing dependency and package management tools for AI agents. Analyze licenses, find outdated packages, visualize dependency trees, estimate bundle sizes, and audit security vulnerabilities.

A Python framework that exposes developer tools through MCP, REST API, and CLI from a single shared registry, enabling AI assistants, HTTP clients, and terminal users to access the same tools.

Enables AI agents to explore, analyze, and search file systems, with tools for directory trees, file statistics, duplicate detection, code counting, and multi-criteria file search.

Enables intelligent code analysis and search across repositories using the CodeRank algorithm (inspired by PageRank) to identify critical modules, trace dependencies, find code hotspots, and perform context-aware keyword searches with importance-ranked results.