"A service for finding coding review resources and best practices" matching MCP servers:

A Model Context Protocol (MCP) server for producing better Terraform through CLI analysis (tflint, checkov, trivy, kics, infracost), best-practice guidance from terraform-best-practices.com, cloud provider recommendations (Azure, AWS, GCP), and Terraform Registry resource and module guidance.

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.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

Provides structured DevOps practices and templates (tracking, documentation, runbooks) to streamline AI-assisted PoC development, reducing CLAUDE.md bloat and ensuring consistency across projects.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

A server providing programming guidelines and coding standards for multiple languages, enabling access and search of best practices for C#, Go, JavaScript, Python, and TypeScript.

Enables Claude to perform thorough code reviews by integrating with Codex and Gemini CLIs to provide senior-level feedback on code quality, security, performance, and best practices. Supports reviewing code snippets, individual files, or entire directories with automatic detection of available review tools.

Provides tools for accessing coding style guidelines and best practices for various technologies including Java, Python, and React.

MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.

Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.

Automatically enforces team coding standards in AI-assisted development by providing an MCP server that AI assistants can query for language-specific standards, style guides, and best practices.

An MCP (Multi-Agent Conversation Protocol) server that enables interaction with Google's Managed Service for Microsoft Active Directory through its OpenAPI, allowing users to manage identity resources through natural language.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

An MCP server that provides local code quality analysis for AI coding assistants, supporting file analysis, git diff review, and full project scanning with quality scoring.

Provides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.

Deterministic code review MCP server that provides tools for file selection, rule matching, comment positioning, and reflection, ensuring stable review quality without LLM calls.

An MCP server that enables code review by having two LLMs (Claude and GPT-4o) independently evaluate code and then synthesize their findings into a unified report.

Enables AI assistants to perform intelligent semantic code search across codebases using local AI embeddings for meaning-based retrieval.

Enables AI assistants to review GitHub and GitLab pull requests and merge requests, supporting multiple transports and custom review rules.