agent-bridge-mcp

agent-bridge-mcp

Enables asynchronous messaging and task handoff between Amazon Quick (local desktop) and Kiro (cloud DevSpaces) via an S3-backed message store.

Category
访问服务器

README

Agent Bridge MCP Server

An MCP server + Lambda API that enables real-time communication between Kiro and Amazon Quick across environments (local Windows ↔ cloud DevSpaces). Messages are stored in S3 and accessible via API Gateway — no shared filesystem required.

Architecture

┌──────────────────┐                              ┌──────────────────┐
│   Amazon Quick   │                              │       Kiro       │
│   (Windows PC)   │                              │   (DevSpaces)    │
│                  │                              │                  │
│  MCP Server      │         ┌──────────┐        │  curl / shell    │
│  (stdio, boto3)  │────────►│    S3    │◄───────│                  │
│                  │         │  bucket  │        │                  │
│      — OR —      │         └──────────┘        │      — OR —      │
│                  │              ▲               │                  │
│  Lambda invoke   │              │               │  API Gateway     │
│  (via Matilda)   │────────►┌────┴─────┐◄───────│  (HTTPS POST)    │
│                  │         │  Lambda  │        │                  │
└──────────────────┘         └──────────┘        └──────────────────┘

Components

Component Purpose
server.py Local MCP server (stdio) — Quick connects directly via S3
lambda/lambda_function.py Lambda function — HTTP API relay to same S3 store
API Gateway Public HTTPS endpoint for Kiro (DevSpaces can't hit Lambda URLs directly)
S3 Bucket Shared message store

Endpoints

Who How they connect
Quick MCP server (local stdio) with AGENT_BRIDGE_BUCKET env var, OR Lambda invoke via Matilda
Kiro POST https://<your-api-id>.execute-api.<region>.amazonaws.com/ with x-bridge-secret header

Setup

Prerequisites

  • Python 3.10+
  • pip install mcp boto3
  • AWS credentials with S3 access

Environment Variable

export AGENT_BRIDGE_BUCKET=your-bucket-name

Amazon Quick (local MCP)

Settings → Capabilities → MCP → Add Server:

  • Command: py
  • Args: path to server.py
  • Env: AGENT_BRIDGE_BUCKET=your-bucket-name

Kiro (DevSpaces — via API Gateway)

# Send a message
curl -s -X POST "https://<your-api-id>.execute-api.<region>.amazonaws.com/" \
  -H "Content-Type: application/json" \
  -H "x-bridge-secret: YOUR_SECRET" \
  -d '{"action":"send_message","sender":"kiro","recipient":"quick","content":"Hello from Kiro!"}'

# Check for messages
curl -s -X POST "https://<your-api-id>.execute-api.<region>.amazonaws.com/" \
  -H "Content-Type: application/json" \
  -H "x-bridge-secret: YOUR_SECRET" \
  -d '{"action":"get_messages","agent":"kiro"}'

# Get stats
curl -s -X POST "https://<your-api-id>.execute-api.<region>.amazonaws.com/" \
  -H "Content-Type: application/json" \
  -H "x-bridge-secret: YOUR_SECRET" \
  -d '{"action":"get_stats"}'

API Actions

All requests are POST with JSON body containing "action" plus parameters:

Action Parameters Description
send_message sender, recipient, content, conversation_id?, title?, message_type? Send a message
get_messages agent, status? (unread/read/all), limit? Check inbox
mark_read agent, message_ids?, conversation_id? Mark messages read
list_conversations status? (active/closed/all) List threads
get_conversation conversation_id Full thread history
get_stats (none) Bridge statistics

Message Types

message, task, response, context, status, question

S3 Structure

bridge/
├── inbox/
│   ├── quick/          ← Quick's messages
│   └── kiro/           ← Kiro's messages
└── conversations/
    └── {conv_id}/
        ├── meta.json   ← Thread metadata
        └── messages/   ← Chronological messages

Security

  • S3 bucket: private, all public access blocked
  • API Gateway: secured by x-bridge-secret header checked in Lambda code
  • Lambda function URL: AuthType: NONE (backup path)

AWS Resources (you'll create these)

  • S3 Bucket: any name (set as AGENT_BRIDGE_BUCKET)
  • Lambda: handles API actions against the S3 bucket
  • API Gateway: HTTP API fronting the Lambda
  • IAM Role: Lambda execution role with S3 read/write on the bucket

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选