by volcengine
A comprehensive collection of Model Context Protocol (MCP) servers that expose Volcengine cloud resources and third‑party services through natural‑language interfaces, enabling AI‑driven operations across compute, storage, databases, networking, security, and developer utilities.
Volcengine MCP Servers offers ready‑to‑use MCP implementations for a wide range of Volcengine cloud services (ECS, TOS, RDS, Redis, CDN, etc.) and popular third‑party tools. Each server translates natural‑language requests into API calls, allowing developers and LLMs to manage cloud resources, query data, and trigger actions without writing code.
Q: Do I need to write code to use these servers? A: No. Once a server is added to an MCP client, you interact via plain‑language commands.
Q: How is authentication handled? A: Most servers require a valid Volcengine API key or token, which you provide through the client’s credential configuration.
Q: Can I run a server locally? A: Yes. Each server can be deployed in a local environment; see its individual README for specific setup instructions.
Q: Are third‑party tools supported? A: Absolutely. The repository includes MCP wrappers for services like GitHub, Slack, Brave Search, and many more.
Q: Where do I find documentation for a specific server?
A: Each server has its own subdirectory (e.g., server/mcp_server_ecs) containing README and usage examples.
火山引擎大模型生态广场的 MCP Server 共享仓库,支持用户探索与体验大模型丰富生态服务,轻松集成全面且易用的工具,同时享受企业级稳定、高效、安全的技术支持,充分释放模型潜力,赋能创新应用开发。
火山引擎大模型生态广场 目前已上线 100+ MCP Server,集成丰富的火山引擎官方云服务及优质三方生态工具。同时支持用户结合火山方舟大模型服务,快速跳转至火山方舟或其他支持 MCP 协议的平台(如 Trae、Cursor、Python 等),助力企业开发者精准打造符合自身业务场景的 AI 大模型应用,打通大模型应用落地的 “最后一公里”。
查看 MCP Server 详情
在火山引擎大模型生态广场,选择合适的 MCP Server,并查看详情。
选择 MCP Server 即将运行的平台
检查当前 MCP Server 已适配的平台,并选择合适的平台。
查看并对比可用的 Tools
仔细查看可用的 Tools 的功能描述与所需的输入参数,并尝试试运行对应的功能。
获取专属的URL或代码示例
检查账号登录状态与服务开通情况,生成唯一 URL 或代码示例。
前往MCP Client 中进行安装与使用
复制 URL 或 JSON,前往支持的MCP Client中进行安装与使用 MCP Server。
MCP Server列表 与 火山引擎大模型生态广场 同步。
volcengine/mcp-server is licensed under the MIT License.
Please log in to share your review and rating for this MCP.
Explore related MCPs that share similar capabilities and solve comparable challenges
by awslabs
Provides specialized servers that expose AWS capabilities through the Model Context Protocol, enabling AI assistants to retrieve up-to-date documentation, execute API calls, and automate infrastructure workflows directly within development environments.
by cloudflare
Provides a collection of Model Context Protocol servers that enable MCP‑compatible clients to interact with Cloudflare services such as Workers, Observability, Radar, and more, allowing natural‑language driven management of configurations, data, and operations.
by Flux159
Connects to a Kubernetes cluster and offers a unified MCP interface for kubectl, Helm, port‑forwarding, diagnostics, and non‑destructive read‑only mode.
by TencentEdgeOne
Deploy HTML, folders, or zip archives to EdgeOne Pages and instantly obtain a public URL for fast edge delivery.
by rishikavikondala
Provides Model Context Protocol tools for performing AWS S3 and DynamoDB operations, with automatic logging and audit access via the `audit://aws-operations` endpoint.
by confluentinc
Enables AI assistants to manage Confluent Cloud resources such as Kafka topics, connectors, and Flink SQL statements through natural‑language interactions.
by aliyun
Enables AI assistants to operate Alibaba Cloud resources such as ECS, Cloud Monitor, OOS and other services through seamless integration with Alibaba Cloud APIs via the Model Context Protocol.
by aws-samples
Retrieve PDF documents and other S3 objects through Model Context Protocol resources, enabling LLMs to pull data directly from AWS S3 buckets.
by heroku
Enables large language models to manage and operate Heroku platform resources directly through the Heroku CLI, exposing a set of tools for app, dyno, add‑on, pipeline, database, and team operations.