MCP-серверы
Открытый реестр серверов Model Context Protocol. Находите инструменты, ресурсы и промпты для ИИ-агентов по категории, типу подключения и сценарию использования.
Серверы
177
Инструменты
471
Категории
11
Участники
155
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 Salesforce that lets AI agents query and modify CRM data using SOQL, manage standard and custom objects (Accounts, Contacts, Opportunities, Cases), describe object metadata, and execute Apex anonymous blocks. Supports both production and sandbox orgs via OAuth or username-password flows.
MCP server for Trello that lets AI agents manage boards, lists, and cards. Supports creating and moving cards, updating due dates and labels, adding comments and checklists, and searching across boards, turning Trello into a conversational task and project tracker.
MCP server for Replicate, which hosts thousands of open machine learning models behind a single API. Lets AI agents search models, run predictions (image, video, audio, and text generation), poll prediction status, and retrieve outputs. Useful for adding generative media and specialized ML capabilities to agent workflows without managing infrastructure.
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.
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.
Official Netlify MCP server for managing web deployments. Lets AI agents create and configure sites, trigger and monitor deploys, manage environment variables, and read build logs. Useful for shipping frontends and serverless functions, debugging failed builds, and automating deployment workflows from an AI assistant.
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 PayPal MCP server for commerce and payments. Lets AI agents create and capture orders, issue invoices, process refunds, manage subscriptions, and query transactions and disputes via the PayPal APIs. Useful for building checkout integrations, automating billing, and reconciling payments from an AI assistant.
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 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 the Reddit API. Lets AI agents search subreddits, fetch hot/new/top posts, read comment threads, and retrieve user activity. Useful for market and community research, sentiment monitoring, trend discovery, and summarizing discussions across communities.
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 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 SonarQube MCP server that brings code quality and security analysis into AI workflows. Lets agents fetch project issues, security hotspots, quality-gate status, and metrics from SonarQube Server or SonarCloud, so code health can be inspected and triaged conversationally.
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 Linear MCP server for project management integration. Enables AI agents to find, create, and update issues, projects, and comments in Linear. Supports searching issues by status, assignee, or label, creating new issues with full metadata, and managing project workflows directly from AI-powered development environments.
MCP server for the Wolfram Alpha computational knowledge engine. Gives AI agents access to step-by-step math, symbolic computation, unit conversions, scientific data, and curated facts across physics, chemistry, finance, and more. Useful for grounding quantitative answers and offloading precise computation from the model.
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.
Official Elastic MCP server that connects AI agents to Elasticsearch data using the Model Context Protocol. Enables natural language interactions with Elasticsearch indices — querying, analyzing, and retrieving data without custom APIs. Supports both stdio and streamable-HTTP transports, and works with Elasticsearch 8.x/9.x clusters including Elasticsearch Serverless. Distributed as a Docker container image from the Elastic registry.
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.
Skills и MCP-серверы
в чём разница?Skillsописывают, что делать
Skill объединяет инструкции, пример промпта и рекомендуемые модели, чтобы агент стабильно выполнял задачу. Skills добавляют знания, а не новые подключения.
MCP-серверыописывают, как подключиться
MCP-сервер расширяет возможности агента, подключая его к реальным системам: базам данных, API и файлам. MCP добавляет подключения и действия, а не инструкции к задаче.
Простое правило: выбирайте Skill, когда модели нужно хорошо выполнить задачу, и MCP-сервер, когда ей нужен доступ к инструменту или системе. Их можно сочетать: Skill может использовать инструменты MCP-сервера.
Создали MCP-сервер?
Добавьте его в открытый реестр, который поддерживает сообщество.