MCP Servers
The open registry for Model Context Protocol servers. Find the right tools, resources, and prompts for your AI agents — filtered by category, transport, or use case.
Servers
160
Tools
465
Categories
11
Contributors
142
Official Browserbase MCP server for cloud browser automation. Lets AI agents create headless browser sessions, navigate pages, extract content, take screenshots, and perform actions using Stagehand, enabling reliable web automation and scraping at scale from AI-powered tools.
Official Apify MCP server that gives AI agents access to thousands of Actors for web scraping and automation. Agents can run Actors to extract data from websites, search engines, social platforms, and maps, then retrieve structured results from the dataset, enabling rich web research and data collection.
Provides comprehensive search capabilities through the Brave Search API via the Model Context Protocol. Enables AI agents to perform web searches, local business searches, image searches, video searches, news searches, and AI-powered summarization. Supports both STDIO and HTTP transports.
Official MCP server for interacting with MongoDB databases and MongoDB Atlas. Enables AI agents to query collections, run aggregations, manage indexes, inspect schemas, and perform CRUD operations. Also supports Atlas cloud management including cluster provisioning, database user management, performance advisor, and stream processing. Supports read-only mode for safe exploration.
MCP server for Sentry error tracking integration. Enables AI agents to retrieve and analyze issues, view error stack traces, search events by query, and access project performance data. Helps developers debug production errors by providing contextual error information directly in AI-powered development workflows.
MCP server for Google Sheets. Lets AI agents read and write cell ranges, create and update spreadsheets and tabs, append rows, and apply formatting via the Sheets API with OAuth. Useful for lightweight data entry, reporting, dashboards, and automating spreadsheet-driven workflows.
MCP server for the ClickUp project management platform. Enables AI agents to create and update tasks, browse spaces, folders, and lists, and manage task status and assignees, bringing ClickUp work management into AI-powered tools.
Official dbt Labs MCP server for analytics engineering workflows. Lets AI agents run dbt commands (build, run, test), inspect models and lineage via the dbt project, and query the Semantic Layer and Discovery API in dbt Cloud. Useful for transforming data, validating models, and answering metric questions grounded in governed definitions.
MCP server for the Databricks Data Intelligence Platform. Enables AI agents to run SQL against the Unity Catalog, inspect schemas and tables, and manage and monitor jobs, bringing lakehouse data and workflows into AI-powered development tools.
Official Heroku MCP server that lets AI agents manage Heroku Platform resources. Supports listing and inspecting apps, scaling dynos, viewing logs, managing config vars and add-ons, and running one-off commands, so deployment and operations tasks can be handled conversationally.
MCP server for the Railway deployment platform. Lets AI agents create projects and services, deploy from repositories, manage environment variables, view deployment logs, and inspect service status. Useful for provisioning backends, databases, and cron jobs and for debugging deploys directly from an AI assistant.
Official DigitalOcean MCP server for managing cloud infrastructure. Lets AI agents deploy and manage App Platform apps, Droplets, databases, and Spaces object storage, and read logs and metrics. Useful for provisioning and operating cloud resources and debugging deployments through an AI assistant.
Provides browser automation capabilities through the Model Context Protocol using Puppeteer. Enables AI agents to navigate web pages, take screenshots, click elements, fill forms, and execute JavaScript in a browser context. Useful for web scraping, testing, and interacting with web applications programmatically. Originally part of the reference servers, now archived and available in servers-archived.
MCP server for Langfuse, the open-source LLM observability and prompt management platform. Enables AI agents to fetch and render managed prompts, list prompt versions, and query traces, helping teams manage prompts and inspect LLM application behavior from AI-powered tools.
Algolia's MCP server for interacting with the Algolia search platform through AI tools. Lets agents run searches against indices, inspect and manage index settings and records, and review analytics and monitoring data using natural language. Useful for building, tuning, and debugging search experiences powered by Algolia.
BrowserStack's official MCP server for AI-assisted testing. Lets agents manage test cases, run manual and automated tests across real browsers and devices, access debugging artifacts such as logs and session details, and triage failures using plain English. Useful for cross-platform QA and browser-compatibility workflows from MCP-enabled clients.
Chrome's official MCP server for inspecting and controlling a live browser through Chrome DevTools. It helps agents diagnose UI, network, performance, and runtime issues.
Cloudinary's official MCP servers for managing media through conversational AI. Cover the full media workflow: uploading and transforming images and videos, organizing assets with structured metadata, configuring processing pipelines, and running AI-powered content analysis. Available as remote OAuth endpoints or local npx processes across several focused servers (asset management, environment config, structured metadata, and analysis).
Confluent's open-source MCP server that connects AI assistants to Confluent Cloud, Confluent Platform, and standalone Apache Kafka deployments. Provides tools to manage Kafka topics and connectors, work with Schema Registry, and run Flink SQL statements through natural language, helping teams operate streaming data platforms from an MCP client.
Community MCP server that provides privacy-friendly web search through DuckDuckGo, plus fetching and parsing of web page content into clean text. Lets AI agents look up current information and retrieve source pages without an API key, making it a lightweight option for research and retrieval workflows. Not affiliated with DuckDuckGo.
Dynatrace's official MCP server that brings the Dynatrace observability platform into AI workflows. Lets assistants query problems and vulnerabilities, run DQL against logs, metrics, and traces, inspect entities, and pull real-time monitoring data directly into a developer's coding environment for faster troubleshooting and root-cause analysis.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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