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
92
Tools
343
Categories
11
Contributors
79
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 CircleCI MCP server. Lets AI agents fetch build and pipeline status, retrieve failed build logs, and diagnose flaky or broken jobs so developers can fix CI failures without leaving their AI-powered tools.
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.
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.
MCP server for the Google Maps Platform. Enables AI agents to geocode addresses, search for places, retrieve place details, and compute directions and distances, giving models location awareness and routing from AI-powered tools.
MCP server for HashiCorp Vault secrets management. Enables AI agents to read and write secrets, list secret paths, and manage key/value engines under controlled policies, so applications and workflows can retrieve credentials securely from AI-powered tools.
MCP server for Jenkins CI/CD. Enables AI agents to trigger builds, inspect job and build status, stream console logs, and diagnose failing pipelines, bringing continuous integration workflows into AI-powered development environments.
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.
Official MCP server for the Milvus vector database. Lets AI agents create collections, insert vectors, and run similarity and scalar-filtered searches over large-scale embedding data, enabling retrieval and long-term memory for AI applications.
Official monday.com MCP server. Lets AI agents read and update boards, items, and columns, create new items, and run queries against the monday.com Work OS so teams can manage work directly from AI-powered tools.
Official PagerDuty MCP server for incident management. Lets AI agents list and triage incidents, acknowledge and resolve them, look up on-call schedules, and query services so responders can manage operational incidents from AI-powered tools.
Official Semgrep MCP server for static application security testing. Lets AI agents scan code for security vulnerabilities and bugs, run custom rules, and return findings with severity and remediation guidance, embedding SAST into AI-powered development workflows.
MCP server for the Telegram Bot API. Lets AI agents send messages, deliver files and photos, and read updates from chats and channels through a bot, enabling notifications and conversational workflows from AI-powered tools.
MCP server for the Weaviate open-source vector database. Enables AI agents to store objects, run semantic and hybrid searches, and manage collections, making it a memory and retrieval backend for RAG applications directly from AI-powered tools.
MCP server that provides access to Wikipedia content. Lets AI agents search articles, fetch full or summarized page content, and resolve references, giving models reliable, citable background knowledge for research and question answering.
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.
MCP server for Atlassian Bitbucket that connects AI tools to repositories, pull requests, branches, and pipelines. Enables agents to review and create pull requests, read file contents and diffs, leave comments, and inspect build status on Bitbucket Cloud and Server.
MCP server for Atlassian Confluence that lets AI agents search the knowledge base, read and create pages, update content, and navigate spaces. Useful for grounding answers in internal documentation and for drafting or maintaining wiki content directly from an AI client.
MCP server for Gmail that lets AI agents read, search, draft, and send email, manage labels, and organize the inbox via the Gmail API with OAuth. Handy for triaging mail, drafting replies, and automating routine inbox workflows from an AI client.
MCP server for Google Calendar that lets AI agents view, create, update, and delete events, check availability across calendars, and schedule meetings via the Calendar API with OAuth. Useful for natural-language scheduling and calendar management.
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.
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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