"How to review merge requests in GitLab" matching MCP servers:

Provides integration between Merge API and LLM providers supporting the MCP protocol, allowing natural language interaction with Merge data across HRIS, ATS, and other categories.

First gitlab mcp for you, building together

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

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.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Get access to real-time SEO data, including: keyword insights, backlink data, traffic estimates and more. Allow AI tools and Large Language Models (LLMs) to tap into the real-time SEO Review Tools API with natural language commands.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

An academic paper management tool that enables multi-source research searching, batch PDF analysis, and the generation of comprehensive, cross-literature reviews. It features intelligent token compression and quality evaluation to streamline the literature review workflow for researchers.

Enables automated GitHub Pull Request reviews using local Ollama, Cursor CLI, or Gemini CLI as AI providers. Supports customizable review prompts, comprehensive PR analysis, and optional auto-posting of reviews to GitHub.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Multi-model adversarial design review MCP server that distributes design documents to multiple LLMs, retrieves project knowledge, and produces calibrated consensus summaries.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.