Aws Knowledge Base MCP Server

AWS知识库MCP服务器

用法

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{
  "mcpServers": {
    "knowledge-base": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://aws-knowledge-base-mcp-server.daohoangson.workers.dev/sse"
      ]
    }
  }
}

{
  "servers": {
    "aws-knowledge-base": {
      "type": "sse",
      "url": "https://aws-knowledge-base-mcp-server.daohoangson.workers.dev/sse"
    }
  }
}



模型上下文协议（MCP）服务器实现，使AI助手能够使用AWS Bedrock和Cloudflare Workers在知识库中搜索。该项目由三个主要部分组成：

建筑

1. 基础设施 (/cdk)：设置资源，包括：

- 用于文档嵌入的AWS基岩知识库 - Pinecone用于高效的文档搜索 - 用于存储文档文件的AWS S3存储桶 - 用于MCP服务器的AWS Lambda功能，支持流式HTTP传输 - AWS IAM用户和API访问策略

1. MCP服务器 (/cloudflare-mcp-server)：实现MCP服务器，该服务器：

- 提供 search_knowledge_base AI助手工具 - 与AWS Bedrock集成以进行文档检索 - 支持SSE（已弃用）和流式HTTP传输 - 在Cloudflare Workers上运行，兼容Node.js

1. MCP客户端 (/ai-sdk-mcp-client)：一个演示客户端，它：

- 用途 Vercel的AI SDK Nova Pro型号 - 充当MCP客户端，支持SSE和流式HTTP传输 - 通过工具调用流式传输文本响应

graph TD
  subgraph AWS
    S3[S3 Bucket] -->|Stores Documents| KnowledgeBase
    TitanModel[Titan Embed Text V2] -->|Embedding Model| KnowledgeBase
    Lambda -->|Retrieve API| KnowledgeBase
    NovaPro[Nova Pro]
  end

  subgraph Pinecone
    KnowledgeBase[Bedrock Knowledge Base] -->|Vector Embeddings| PineconeDb[Vector Database]
  end

  subgraph Cloudflare
    Worker -->|Retrieve API| KnowledgeBase
  end

  Demo -->|InvokeModel API| NovaPro
  Demo -->|MCP
Streamable HTTP| Lambda
  Demo -->|MCP
Server Sent Event| Worker
  Demo -->|MCP
Streamable HTTP| Worker

基础设施

设置

cd cdk

npm install

# Unique identifier for the CDK stack
export CDK_APP_ID="DocsMcpServer"

# Pinecone credentials to manage vector databases
export PINECONE_API_KEY="pcsk_foo"

# Deploy the stack
npx cdk deploy

输出示例

DocsMcpServer.AwsAccessKeyId = AKI123
DocsMcpServer.AwsRegion = us-east-1
DocsMcpServer.AwsSecretAccessKey = 4cQ456
DocsMcpServer.DataSourceId = OD6LTXXUNH
DocsMcpServer.DocsBucketName = docsmcpserver-docsbucketa5ce02e3-gg8g3crhlo1j
DocsMcpServer.KnowledgeBaseId = GZUYX1EGMF
DocsMcpServer.McpServerUrl = https://vc7ejtu4kk3ayeiqofkmxxzada0uwpzr.lambda-url.us-east-1.on.aws/

文档更新

这 /docs 目录包含用最新文档更新知识库的脚本：

cd docs

# Set environment variables from the CDK output
export DATA_SOURCE_ID="OD6LTXXUNH"
export DOCS_BUCKET_NAME="docsmcpserver-docsbucketa5ce02e3-gg8g3crhlo1j"
export KNOWLEDGE_BASE_ID="GZUYX1EGMF"

# Run the update script
./update.sh

这将：

1. 从CloudFlare和MCP网站下载最新文档
2. 将文件上传到S3
3. 启动新的摄取作业以更新知识库

MCP服务器

MCP服务器提供 search_knowledge_base AI助手可以使用该工具搜索索引文档。该工具接受查询字符串并返回相关文档。

创建 .dev.vars CDK输出中的以下变量：

AWS_ACCESS_KEY_ID=AKI123
AWS_REGION=us-east-1
AWS_SECRET_ACCESS_KEY=4cQ456
KNOWLEDGE_BASE_ID=GZUYX1EGMF

设置

cd cloudflare-mcp-server

npm install

npm run cf-typegen

# run locally
npm run dev

# or deploy to Cloudflare
npm run deploy

MCP客户端

客户端演示了如何连接到MCP服务器，并使用其支持SSE和流式HTTP传输的工具。

设置

cd ai-sdk-mcp-client

npm install

# Set environment variables from the CDK output
export AWS_ACCESS_KEY_ID="AKI123"
export AWS_REGION="us-east-1"
export AWS_SECRET_ACCESS_KEY="4cQ456"

# Test connection to CloudFlare with SSE transport
npm start -- https://aws-knowledge-base-mcp-server.daohoangson.workers.dev/sse

# Test connection to CloudFlare with streamable HTTP transport
npm start -- https://aws-knowledge-base-mcp-server.daohoangson.workers.dev/mcp

# Test connection to Lambda
npm start -- https://vc7ejtu4kk3ayeiqofkmxxzada0uwpzr.lambda-url.us-east-1.on.aws/mcp

输出示例

Created MCP client

{ tools: [ 'search_knowledge_base' ] }

 To determine if CloudFlare works with the Model Context Protocol (MCP), I need to search the knowledge base for relevant documentation. 

Tool call: search_knowledge_base {
  "query": "Does CloudFlare work with Model Context Protocol (MCP)?"
}

Tool result

{
  "content": [
    {
      "type": "text",
    },
    {
      "type": "text",
      "text": "can call.  When you build MCP Servers with the `@cloudflare/model-context-protocol` package, you can define tools the ."
    },
    {
      "type": "text",
      "text": "See our [list of example servers](https://modelcontextprotocol.io/examples), or [get started building your own server](https://modelcontextprotocol.io/quickstart/server).   # Introduction Source: https://modelcontextprotocol.io/introduction  Get started with the Model Context Protocol (MCP)  C# SDK released! Check out [what else is new.](/development/updates)  MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.  ## Why MCP?  MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:  * A growing list of pre-built integrations that your LLM can directly plug into * The flexibility to switch between LLM providers and vendors * Best practices for securing your data within your infrastructure  ### General architecture  At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:  ```mermaid flowchart LR     subgraph \"Your Computer\"         Host[\"Host with MCP Client\\n(Claude, IDEs, Tools)\"]         S1[\"MCP Server A\"]         S2[\"MCP Server B"
    },
    {
      "type": "text",
    },
    {
      "type": "text",
      "text": "For instance, if the client doesn't have access to Claude models but has Gemini, it might map the sonnet hint to `gemini-1.5-pro` based on similar capabilities.  ## Error Handling  Clients **SHOULD** return errors for common failure cases:  Example error:  ```json {   \"jsonrpc\": \"2.0\",   \"id\": 1,   \"error\": {     \"code\": -1,     \"message\": \"User rejected sampling request\"   } } ```  ## Security Considerations  1. Clients **SHOULD** implement user approval controls 2. Both parties **SHOULD** validate message content 3. Clients **SHOULD** respect model preference hints 4. Clients **SHOULD** implement rate limiting 5. Both parties **MUST** handle sensitive data appropriately   # Specification Source: https://modelcontextprotocol.io/specification/2025-03-26/index    [Model Context Protocol](https://modelcontextprotocol.io) (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need."
    }
  ]
}

是的，CloudFlare支持模型上下文协议（MCP）。您可以在CloudFlare上构建和部署MCP服务器。文档表明，CloudFlare提供了包和示例，以帮助您开始使用MCP服务器。 有关更多详细信息，请参阅 CloudFlare上的模型上下文协议文档.

成本估算

假设：

• us-east-1 区域
• 共计2 000份文件
• 每个文档约为5KB/约1250个令牌
• 每月重新索引所有内容30次（实际实施将逐步完成）
• 每月使用3000个请求
• 每个请求占用5ms CPU时间/200ms墙时间
• 每个查询大约有100个令牌
• 每个请求返回10个文档

• https://aws.amazon.com/bedrock/pricing/
• https://aws.amazon.com/s3/pricing/
• https://aws.amazon.com/lambda/pricing/
• CloudFlare标准计划包括 10M请求和3K CPU秒.
• 松果标准计划包括 每月15美元的使用积分.替代方案：

- Aurora PostgreSQL无服务器，每月180美元，配备1个写入器、1个读取器、2个NAT网关等。（参见 分支 aurora) - Amazon OpenSearch Serverless最低每月350美元，因为它至少需要一个索引OCU和一个搜索OCU 0.24美元/OCU小时