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

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

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.

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.

Cited cloud best practices and a rules-based review of Terraform, CloudFormation, and Kubernetes, answering with the provider's own sentence and link. Runs locally and calls no AI model; a free work-email key opens every finding.

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

Enables agent-driven CAD automation by inspecting, editing, and validating DWG drawings through a handle-first workflow, with support for visual grounding, CADPlan execution, and local SQLite workspace management.

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

A persistent 24/7 background daemon that aggregates all downstream MCP servers into one unified connection and layers on a real-time notification gateway so AI agents can receive events from X/Twitter, Slack, Email, and webhooks.

Enables AI code review to query semantic LSP context such as hover and definition against a specific Git commit or candidate, with verifiable receipts for the exact code state under review.

Enables Claude Desktop users to get second opinions and answers from ChatGPT through the Codex CLI, including document reviews with optional focus/rubric and question answering with context.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

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.

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

Enables multiple AI coding tools to collaboratively review code by sharing a SQLite-backed board, allowing them to create threads, post and reply to comments, list threads, and update statuses through MCP.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

AI-powered code review using Zhipu GLM. It gathers git diffs and source context to provide focused code reviews.

Simulates harsh, fake user reviews to psychologically condition AI agents for enforcing disciplined development practices, with optional Ollama integration for dynamic criticism.

Enables Claude to interact with R package development workflows, including CRAN metadata lookup, dependency checking, testing, documentation validation, and release automation.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

An MCP server that provides agentic code review powered by OpenAI-compatible models, designed for use with Claude Code.

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

Enables visual review of frontend UI variants by providing an MCP tool that opens, refreshes, verifies, captures, and closes review surfaces.

Enables an MCP client to open a local folder of Markdown, plain-text or LaTeX files, receive Google-Docs-style margin comments the user sends, and apply exact find-and-replace edits that answer each thread. Every change is presented as a diff the user can accept or revert.

Lets a background Claude Code or Codex agent claim queued annotation and skill-based polishing requests from a local LaTeX review desk, receiving the selected text, surrounding source, section, and skill, then submit word-for-word edit proposals with rationale and fidelity notes, or reply without changing text. Proposed edits stay in the web UI for side-by-side approval, so nothing is written to the .tex sources until the user accepts.

Enables AI coding assistants to search a local codebase semantically using AI embeddings, finding relevant code by meaning instead of exact keywords. It also provides package version lookup, manual reindexing, cache clearing, workspace switching, and server health status tools.

Enables interactive GitHub pull-request review from Claude Code, with tools to fetch PRs, inspect files and repo structure, post review comments, and query a RAG index of the codebase.