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Volcengine Model Context Protocol (MCP) Server Marketplace

repository·main·Indexed 43 days ago

https://github.com/volcengine/mcp-server

A marketplace of MCP Servers that connect AI models to Volcengine cloud services and third-party tools. It provides specialized servers for Computing (ECS), Storage (TOS), Databases (RDS MySQL, Redis, MongoDB), Big Data (ByteHouse), and developer tools like Browser-Use and Puppeteer. Supports integration with MCP-compatible clients including Volcengine Ark, Trae, Cursor, and Python.

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What's inside Volcengine MCP Server

  1. Overview of VKE MCP Server

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    The VKE (Volcengine Kubernetes Engine) MCP Server implements the Model Context Protocol, allowing MCP clients (such as Ark, Trae, or Cursor) to interact with Volcengine VKE services using natural language. It enables cluster management, node and node pool management, component management, and K8s resource operations (including applying YAML configurations).

  2. Overview of CR MCP Server

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    The CR MCP Server is a Model Context Protocol (MCP) server that enables MCP clients (such as Cursor or Claude Desktop) to interact with Volcano Engine's Container Registry (CR) service. It allows users to manage CR resources—including registry instances, namespaces, and OCI artifact repositories—using natural language.

    Key capabilities include:

    • Creating and querying registry instances.
    • Creating and querying namespaces.
    • Creating and querying OCI artifact repositories.
    • Querying image tags and registry domains.
    • Retrieving temporary registry passwords.

  3. Overview of VPN Connection MCP Server

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    The VPN Connection MCP Server is a Model Context Protocol (MCP) server that enables MCP clients (such as Cursor or Claude Desktop) to interact with Volcano Engine's VPN services. It allows users to manage and query VPN resources—including VPN gateways, IPsec connections, user gateways, and SSL VPN servers—using natural language or direct JSON requests. This helps efficiently manage cloud network connections through an AI interface.

  4. Overview of DCDN MCP Server Tools

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    The DCDN MCP Server allows for natural language queries to analyze Volcengine DCDN (Dynamic Content Delivery Network) domain configuration and monitoring data. It provides two main categories of tools:

    Domain Configuration Query

    Used to inspect how domains are set up and manage certificates:

    • ListDomainConfig: Query domain configuration.
    • DescribeDomainConfig: Query detailed domain configuration.
    • DescribeDomainDetail: Query detailed configuration of a single domain.
    • ListCert: Query the certificate list.

    Business Data Query

    Used to retrieve traffic, usage, and ranking statistics:

    • DescribeStatistics: Resource usage for access.
    • DescribeOriginStatistics: Resource usage for origin pull.
    • DescribeDomainRegionData: Regional distribution statistics.
    • DescribeDomainIspData: ISP distribution statistics.
    • DescribeTopDomains: Domain ranking statistics.
    • DescribeTopURLs: URL ranking statistics.
    • DescribeTopIPs: IP ranking statistics.
    • DescribeTopReferers: Referer ranking statistics.
    • DescribeDomainPVData: PV (Page View) statistics.
    • DescribeDomainUVData: UV (Unique Visitor) statistics.
    • DescribeRealtimeData: Real-time access monitoring.
    • DescribeOriginRealtimeData: Real-time origin pull monitoring.

  5. Overview of Volcano Engine Cloud Phone (ACEP) - Mobile Use MCP Server

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    The Mobile Use MCP Server is an AI Agent solution designed for automated tasks in mobile Android environments using Volcano Engine's Cloud Phone (ACEP). It implements the Model Context Protocol (MCP), allowing AI agents to interact with cloud phones through a standardized set of tools. This enables automated execution of multi-concurrency and asynchronous tasks, such as app installation, screen interaction, and navigation.

  6. Overview of Real-time Conversational AI MCP

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    The mcp-server-rtc provides a real-time conversational AI service similar to Doubao. It integrates Large Language Models (LLM), Vision Language Models (VLM), Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and audio/video processing/transmission capabilities. It is designed for scenarios like AI assistants, customer service, AI companionship, language teaching, and smart hardware, enabling natural, human-like real-time voice interactions.

  7. Overview of project MCP Server

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    The project MCP Server is a tool-based server that provides resource grouping management for Volcano Engine. It allows for logical resource isolation, project-based authorization, and billing operations.

    Available Tools:

    • CreateProject: Create a new project.
    • GetProject: Retrieve project details.
    • ListProjectIdentities: Get a list of project identity entities.
    • ListProjectResources: List resources associated with a project.
    • ListProjects: List all projects.

  10. Overview of VPC MCP Server

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    The VPC MCP Server is a Model Context Protocol (MCP) server that enables MCP clients (such as Trae, Cursor, or Ark) to interact with Volcano Engine's Virtual Private Cloud (VPC) services. It allows for natural language-based VPC resource management, specifically supporting query operations for resources like private networks, subnets, route tables, network interfaces, security groups, and more.

  11. Overview of VMP MCP Server

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    The VMP MCP Server provides a Model Context Protocol interface for the Managed Prometheus Service (VMP). It allows users to perform Prometheus workspace queries and metric queries using natural language. This is designed to assist in operations troubleshooting and data analysis scenarios by bridging natural language inputs with Prometheus metric data.

  12. Overview of VOD MCP Server

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    The VOD MCP Server (also known as VeVOD MCP) is a Model Context Protocol (MCP) plugin provided by Volcano Engine Video on Demand. It exposes professional cloud-based video capabilities as tools that AI agents can invoke via natural language.

    Key capabilities include:

    • Professional cloud-based video editing
    • Video quality enhancement
    • Content recognition
    • Other atomic video processing tasks

    This allows developers to build AI agents capable of performing complex video processing workflows through simple text commands.