"A tool or service to review videos and provide insights or summaries" matching MCP servers:

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

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.

A read-only MCP server for the Bitbucket Cloud REST API v2.0 that enables AI assistants to browse workspaces, repositories, and review pull requests.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

16-tool MCP server for Google PageSpeed Insights & Chrome UX Report APIs. Analyze, compare, and optimize web performance directly through Claude, Cursor, or any MCP-compatible AI client.

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.

Runs Google PageSpeed Insights audits against any public URL, returning Lighthouse scores, lab timing metrics, ranked fixes, and real-user Chrome UX Report field data.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

This MCP server integrates Google PageSpeed Insights to analyze website performance, accessibility, best practices, SEO, and PWA on mobile and desktop, returning detailed audits and optimization opportunities.

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.

A collaborative code and markdown review tool that bridges human reviewers and AI agents, enabling both to browse files, inspect git diffs, leave structured comments, and save a final review report from the same UI in real time.

A code review tool that generates detailed HTML review documents by comparing branches or commits, with author filtering and intelligent analysis.

A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.

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.

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 review GitHub and GitLab pull requests and merge requests, supporting multiple transports and custom review rules.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

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