Amazon Detective MCP Server Integration Guide
Section A: Quick Answer & Architectural Summary
The Amazon Detective Model Context Protocol (MCP) integration bridges AI coding assistants to the Amazon Detective ai & ml API. It exposes 10 validated endpoint operations as callable tools for Claude Desktop, Cursor, and VS Code. Configuration is managed via hosted registry at /config/amazonaws-com-detective.json or local stdio bridge execution. Operates with zero authentication credentials out of the box. Contains 10 mutating operations (POST/PUT/DELETE); user confirmation is recommended before triggering write operations.
MCPBridge Editorial Verdict: Amazon Detective
AI coding workflows requiring programmatic access to Amazon Detective (AI & ML) endpoints
Low (1-2 mins)
Zero Authentication Required
Automated Spec Tracking
Claude Desktop, Cursor IDE, VS Code (Cline), Zed Editor
Read & Mutating endpoints; client confirmation and least-privilege token recommended
MCPBridge rates Amazon Detective as a standardized OpenAPI-to-MCP bridge providing structured tool definitions across 10 endpoints.
Technical Overview & Protocol Integration
Amazon Detective is a fully managed security service provided by Amazon Web Services (AWS) that employs machine learning, statistical analysis, and graph theory to automatically collect, normalize, and analyze log data from critical AWS workloads. Its core capability lies in transforming raw, disconnected logs from services like Amazon CloudTrail, VPC Flow Logs, and Amazon GuardDuty into interactive, correlated visualizations. These visualizations provide a cohesive view of the underlying network, user, and API activity across an account or organization over time. Typical use cases are centered on security operations (SecOps) and incident response within enterprise environments. Security analysts and incident responders use Detective to rapidly investigate potential security findings—such as unusual API call patterns, instance connection attempts, or compromised credentials—by understanding the context, timeline, and impact of these events without manually querying disparate log sources. It significantly reduces the mean time to resolution (MTTR) for security incidents by providing a pre-built investigative framework.
When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), the Amazon Detective API gains a powerful new interface for programmatic and automated security analysis. An AI model with access to this MCP server can act as an intelligent security analyst co-pilot. Instead of a developer manually writing AWS CLI commands or navigating the console, they can issue natural language instructions to the AI. The AI, leveraging its understanding of the API's structure and purpose, translates these instructions into precise API calls. This integration provides immense value by enabling rapid, automated querying of complex security graphs and relationships, automating routine data source collection tasks, and programmatically managing investigation scopes and access—all within a development or incident response workflow. It bridges the gap between complex security data and actionable, automated insights.
A developer can instruct an AI agent to perform dynamic, context-aware security tasks using this MCP server. For example, a developer could ask, "AI agent, query the activity graph for the IAM user 'dev-lead' over the last 24 hours and summarize any calls made to Amazon S3 outside our standard IP range," prompting the AI to use the graph-related endpoints to fetch and analyze the relevant nodes and edges. Another workflow could be: "AI agent, update the membership for Detective to include all accounts in our 'Workloads' OU and ensure the primary data sources are enabled." This would instruct the AI to automate the configuration of Detective across multiple accounts. Furthermore, a developer could say, "AI agent, describe the current organization configuration for Detective and disable administrative access for the 'audit-role' account to follow our least privilege policy," allowing the AI to programmatically enforce security governance.
Critical to implementing this integration securely is the authentication and configuration layer. Although the provided API listing specifies "None" for authentication, this represents the interface to the MCP server itself. All calls from the MCP server to the actual AWS Detective service must be authorized using AWS Identity and Access Management (IAM). Developers must create an IAM role with precisely scoped permissions—following the principle of least privilege—that grants the MCP server only the necessary Detective actions (e.g., detective:Graph, detective:UpdateMembership). The MCP server should be configured to use this role via an AWS profile or environment variables. Furthermore, developers should ensure that all sensitive data and access tokens are handled securely, and that the MCP server's endpoint is not exposed to the public internet. All investigative actions and configuration changes executed by the AI agent should be logged and auditable, reinforcing a robust security posture even while leveraging powerful automation.
By translating the OpenAPI 3.0 specification for Amazon Detective into native Model Context Protocol (MCP) tool definitions, developers and AI agents gain programmatic access to endpoints over stdio or HTTP transports. Every endpoint is translated into a discrete tool payload complete with input argument validation, parameter descriptions, and return type definitions.
2. Technical Specifications Matrix
System Specifications
| API Name | Amazon Detective |
| Slug Identifier | amazonaws-com-detective |
| Category | AI & ML |
| Auth Method | None Required |
| Endpoint Count | 10 tools mapped |
| Spec Version | OpenAPI v2018-10-26 |
| Transport Type | STDIO |
| Publisher Source | auto |
3. Multi-Client Installation Matrix
Copy and paste these pre-formatted JSON snippets into your MCP client configuration files.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"amazonaws-com-detective": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/detective/2018-10-26/openapi.json"
],
"env": {
"AMAZON_DETECTIVE_API_KEY": "your_amazon_detective_api_key"
}
}
}
}Cursor IDE
Settings → MCP Servers → Add Hosted Config
{
"mcpServers": {
"amazonaws-com-detective": {
"url": "https://mcpbridge.org/config/amazonaws-com-detective.json"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code / Cline
Use with MCP extension config
{
"mcpServers": {
"amazonaws-com-detective": {
"url": "https://mcpbridge.org/config/amazonaws-com-detective.json"
}
}
}4. Security Architecture & Credentials Reference
Key parameters and credential variable mappings for Amazon Detective.
Security Considerations & Sandbox Guidance: Amazon Detective
Authorization credential isolation, least privilege boundaries, and container sandboxing options.
None Required
Read & Mutating Operations
Local MCP bridge process making outbound HTTPS requests to upstream API
Isolation & Principle of Least Privilege
Ensure outbound network access to the API endpoint is permitted. Use restricted API tokens with minimal read/write scopes.
Actionable Operational Guidelines
- Verify network firewall rules allow outbound traffic to upstream API endpoints.
- Review arguments for mutating endpoints (/invitation, /graph/datasources/get, /membership/datasources/get) before execution.
- Apply token rate limits and monitor usage in your provider dashboard to prevent unexpected quota consumption.
| Variable Name | Required | Example Value |
|---|---|---|
| AMAZON_DETECTIVE_API_KEY | REQUIRED | your_amazon_detective_api_key |
5. Endpoints & Tool Schemas Matrix
Search and inspect the 10 tool signatures mapped from OpenAPI.
Executable Code Integration Examples
Call Amazon Detective endpoints via cURL, TypeScript, or Python REST SDKs.
curl -X PUT "https://api.apis.guru/v2/specs/amazonaws.com/detective/2018-10-26/invitation" \ -H "Content-Type: application/json" \ # No auth required
Concrete Real-World Use Cases for Amazon Detective
Practical multi-step agentic workflows and prompt directives demonstrating concrete developer outcomes.
Automated Contextual Workflow Integration
A developer can instruct an AI agent to perform dynamic, context-aware security tasks using this MCP server. For example, a developer could ask, "AI agent, query the activity graph for the IAM user 'dev-lead' over the last 24 hours and summarize any calls made to Amazon S3 outside our standard IP range," prompting the AI to use the graph-related endpoints to fetch and analyze the relevant nodes and edges. Another workflow could be: "AI agent, update the membership for Detective to include all accounts in our 'Workloads' OU and ensure the primary data sources are enabled." This would instruct the AI to automate the configuration of Detective across multiple accounts. Furthermore, a developer could say, "AI agent, describe the current organization configuration for Detective and disable administrative access for the 'audit-role' account to follow our least privilege policy," allowing the AI to programmatically enforce security governance.
- AI assistant inspects prompt context and selects relevant tool
- Validates parameter payload against OpenAPI JSON Schema
- Executes tool call and formats structured API response
Automated Mutation & Resource Creation
Execute state changes and create records through PUT operations like "/invitation" with parameter validation.
- Agent constructs validated request body matching schema
- Prompts user for execution confirmation
- Executes tool and confirms response status
Good Fit vs. Poor Fit Criteria for Amazon Detective
Architectural guidelines to determine when to adopt this integration and when to explore alternatives.
When to Choose / Good Fit
- AI coding assistants in Claude Desktop or Cursor requiring structured tool access to Amazon Detective.
- Developers who want standardized OpenAPI-to-MCP translation without building custom server code.
- Workflows that benefit from automated parameter validation against official OpenAPI 3.0 schemas.
- Teams seeking zero-maintenance hosted JSON configurations for easy distribution.
When to Avoid / Poor Fit
- Ultra-high frequency data ingestion exceeding typical LLM context windows and token rate limits.
- Unattended autonomous agent loops with write access where human approval of mutations is mandatory.
- Environments lacking outbound internet access to upstream Amazon Detective API servers.
Verification & Evidence Audit: Amazon Detective
OpenAPI 3.0 specification parsed and validated via automated build pipeline.
Independent Evidence Checks
Valid specification version 2018-10-26 with 10 endpoints indexed.
No authentication required.
JSON Schemas mapped to MCP tools/call standard format.
Automated schema validation only; live upstream API calls require developer credentials.
Project Health & Maintenance Audit: Amazon Detective
ActiveActivity & Cadence
Transparent Quality Score Breakdown
Alternatives & Comparison Table (AI & ML)
Comparative trade-offs between Amazon Detective and similar ecosystem tools in the AI & ML category.
| Option | Best For | Main Difference vs. Amazon Detective | Setup / Runtime | Explore |
|---|---|---|---|---|
| Amazon Augmented AI Runtime | Developers needing AI & ML operations with 5 tools | 5 endpoints vs 10 endpoints | auto / v2019-11-07 | View → |
| Amazon CodeGuru Profiler | Developers needing AI & ML operations with 10 tools | 10 endpoints vs 10 endpoints | auto / v2019-07-18 | View → |
| Amazon CodeGuru Reviewer | Developers needing AI & ML operations with 10 tools | 10 endpoints vs 10 endpoints | auto / v2019-09-19 | View → |
9. Error Resolution & Troubleshooting Guide
Contextual diagnostics for HTTP status codes and JSON-RPC tool bridge operations.
-32600 (Invalid Request)Root Cause: Malformed JSON-RPC payload sent to local MCP bridge process.
Resolution Action: Verify MCP client payload adheres to JSON-RPC 2.0 specification.
-32601 (Method Not Found)Root Cause: Requested operation does not exist in mapped Amazon Detective OpenAPI endpoint schemas.
Resolution Action: Inspect Section 5 endpoints table to confirm valid method names and paths.
-32602 (Invalid Params)Root Cause: Missing or invalid parameters for target tool operation.
Resolution Action: Check parameter data types against OpenAPI JSON Schema specification.
429 Rate Limit ExceededRoot Cause: Upstream Amazon Detective API request rate limit quota reached.
Resolution Action: Implement exponential backoff in tool execution loop or verify provider plan quotas.
OPENAPI_GATEWAY_TIMEOUTRoot Cause: Upstream Amazon Detective endpoint response latency exceeded timeout threshold.
Resolution Action: Verify network connectivity and check provider system status dashboard.
Official Verified Sources for Amazon Detective
Authoritative upstream repositories, specifications, package registries, and configuration endpoints.
Official Upstream Documentation
Official developer documentation and API reference for Amazon Detective.
https://docs.aws.amazon.com/detective/OpenAPI 3.0 Specification
Machine-readable OpenAPI schema source used for MCP tool mapping.
https://api.apis.guru/v2/specs/amazonaws.com/detective/2018-10-26/openapi.jsonHosted MCPBridge Configuration
Pre-generated Model Context Protocol JSON configuration hosted on MCPBridge.
https://mcpbridge.org/config/amazonaws-com-detective.jsonOpenAPI-to-MCP Converter Tool
Client-side browser converter to customize or filter endpoint tools.
https://mcpbridge.org/convert/Claim & Maintainer Verification
Submit a claim to verify API publisher ownership and update metadata.
https://github.com/stormlive-ai/mcp-bridge-docs/issues/new?title=Claim+Listing%3A+Amazon+Detective+%28api%3A+amazonaws-com-detective%29&labels=claim-listing&body=%23%23+Claim+Listing+Request%0A%0AI+would+like+to+claim+this+listing%3A%0A%0A-+**Type%3A**+api%0A-+**ID%3A**+amazonaws-com-detective%0A-+**Name%3A**+Amazon+Detective%0A%0A%23%23%23+Your+Information%0A%0A**GitHub+Handle%3A**+%3C%21--+your+GitHub+username+--%3E%0A%0A**Email%3A**+%3C%21--+optional%2C+for+verification+--%3E%0A%0A**Relationship+to+this+API%3A**%0A-+%5B+%5D+I+am+the+API+provider+%2F+maintainer%0A-+%5B+%5D+I+am+an+authorized+representative%0A-+%5B+%5D+Other%3A%0A%0A%23%23%23+Verification+Method%0A-+%5B+%5D+I+will+add+a+CNAME%2FTXT+record+to+verify+domain+ownership%0A-+%5B+%5D+I+can+confirm+from+an+email+address+at+the+provider+domain%0A-+%5B+%5D+I+maintain+the+GitHub+repository%0A%0A%23%23%23+Updates+I%27d+Like+to+Make+%28optional%29%0A%3C%21--+What+would+you+like+to+update%3F+Description%2C+links%2C+category%2C+etc.+--%3E%0A%0A---%0A*Submitted+via+MCP-Bridge+claim+form*Frequently Asked Technical Questions: Amazon Detective
Targeted developer questions regarding installation, client configuration, credentials, and error resolution.
The Amazon Detective MCP server connects AI coding assistants (Claude Desktop, Cursor, VS Code, Zed) to the Amazon Detective API using the Model Context Protocol. It converts 10 OpenAPI operations into native MCP tools callable during chat sessions.