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.github/CODEOWNERS

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/src/prometheus-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @MohamedSherifAbdelsamiea
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/src/redshift-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @grayhemp
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/src/s3-tables-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @gsoundar @gregorywright @Kurtiscwright @hsingh574 @ananthaksr @okhomin @Zh111602
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/src/sagemaker-ai-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @dparkar @githuboston @jade710 @JunqiYe
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/src/stepfunctions-tool-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @mmouniro
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/src/syntheticdata-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @pranjbh
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/src/terraform-mcp-server @awslabs/mcp-admins @awslabs/mcp-maintainers @alexa-perlov

.secrets.baseline

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"filename": "src/aws-iac-mcp-server/README.md",
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"hashed_secret": "df99ad98cabfe1616640820bcfb345ef5b10077f",
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"is_verified": false,
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"line_number": 168,
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"line_number": 290,
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"is_secret": false
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}
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],
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}
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]
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},
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"generated_at": "2025-11-20T23:43:13Z"
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"generated_at": "2025-11-26T15:26:15Z"
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}

README.md

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---
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title: Amazon SageMaker AI MCP Server
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---
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import ReadmeContent from "../../../src/sagemaker-ai-mcp-server/README.md";
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<div className="readme-content">
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<style>
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{`
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.readme-content h1:first-of-type {
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display: none;
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}
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`}
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</style>
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<ReadmeContent />
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</div>

docusaurus/sidebars.ts

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"servers/nova-canvas-mcp-server",
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"servers/aws-bedrock-custom-model-import-mcp-server",
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"servers/amazon-bedrock-agentcore-mcp-server",
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"servers/sagemaker-ai-mcp-server",
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],
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},
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{

docusaurus/static/assets/server-cards.json

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"vibe-coding"
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]
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},
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{
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"category": "AI & Machine Learning",
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"description": "SageMaker AI resource management and model development",
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"icon": "\ud83c\udfd7\ufe0f",
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"id": "sagemaker-ai-mcp-server",
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"name": "Amazon SageMaker AI MCP Server",
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"source_path": "src/sagemaker-ai-mcp-server/",
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"subcategory": "Model Orchestration",
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"tags": [
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"ai-ml",
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"model-development",
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"vibe-coding",
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"conversational",
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"machine-learning",
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"ai-tools",
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"serverless"
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],
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"workflows": [
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"autonomous",
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"conversational",
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"vibe-coding"
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]
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},
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{
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"category": "Data & Analytics",
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"description": "Complete DynamoDB operations and table management",

src/amazon-bedrock-agentcore-mcp-server/README.md

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- `error`: Error message (if fetch failed)
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Use this tool to get complete documentation pages when search snippets aren't sufficient for understanding or implementing AgentCore features.
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### manage_agentcore_runtime
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Provides comprehensive information on deploying and managing agents in AgentCore Runtime.
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```python
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manage_agentcore_runtime() -> Dict[str, Any]
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```
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**Returns:**
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Detailed deployment guide covering:
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- Code requirements and validation checklist
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- Step-by-step CLI deployment workflow (configure, launch, invoke, status, destroy)
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- Required code patterns with BedrockAgentCoreApp
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- Common issues and troubleshooting
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- Session management and cleanup procedures
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Use this tool when you need to deploy agents to AgentCore Runtime or troubleshoot deployment issues.
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### manage_agentcore_memory
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Provides comprehensive information on managing AgentCore Memory resources.
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```python
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manage_agentcore_memory() -> Dict[str, Any]
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```
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**Returns:**
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Complete memory management guide covering:
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- Memory resource creation and configuration
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- Short-term memory (STM) and long-term memory (LTM) concepts
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- Semantic memory strategies for facts and knowledge
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- Full CLI command reference (create, get, list, delete, status)
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- Common workflows and examples
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Use this tool when working with AgentCore Memory for persistent knowledge storage.
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### manage_agentcore_gateway
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Provides comprehensive information on deploying and managing MCP Gateways in AgentCore.
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```python
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manage_agentcore_gateway() -> Dict[str, Any]
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```
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**Returns:**
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Complete gateway deployment guide covering:
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- Gateway creation and configuration requirements
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- Step-by-step CLI deployment workflow
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- Target management for Lambda, OpenAPI, and Smithy models
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- Authentication and authorization setup (Cognito, OAuth2, API keys)
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- Management commands (list, get, delete)
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- Common patterns and troubleshooting
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Use this tool when deploying MCP Gateways to provide managed endpoints for Model Context Protocol servers.

src/amazon-bedrock-agentcore-mcp-server/awslabs/amazon_bedrock_agentcore_mcp_server/__init__.py

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"""awslabs.amazon-bedrock-agentcore-mcp-server"""
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__version__ = '0.0.5'
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__version__ = '0.0.6'

src/amazon-bedrock-agentcore-mcp-server/pyproject.toml

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name = "awslabs.amazon-bedrock-agentcore-mcp-server"
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# NOTE: "Patch"=9223372036854775807 bumps next release to zero.
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version = "0.0.5"
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version = "0.0.6"
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description = "Model Context Protocol (MCP) server for Amazon Bedrock AgentCore services"
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readme = "README.md"

src/amazon-bedrock-agentcore-mcp-server/uv.lock

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