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MCP-Bridge
Cloud InfrastructureAuto-generatedScore: 46

Access Analyzer MCP Server

The AWS Identity and Access Management Access Analyzer API provides a powerful, policy-as-code service that automatically identifies resources accessible from outside your AWS account or organization.

Quick Start Summary

The Access Analyzer MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Access Analyzer API through natural language. It exposes 10 API endpoints as callable tools, such as ApplyArchiveRule, GetGeneratedPolicy, CancelPolicyGeneration, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-accessanalyzer. This integration is sourced from the auto Access Analyzer OpenAPI specification (v2019-11-01) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Cloud Infrastructure
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2019-11-01
Install Command
npx -y @mcp/amazonaws-com-accessanalyzer

Environment Variables

ACCESS_ANALYZER_API_KEY

Example: your_access_analyzer_api_key

Top Endpoints

PUT
/archive-rule

ApplyArchiveRule

GET
/policy/generation/{jobId}

GetGeneratedPolicy

PUT
/policy/generation/{jobId}

CancelPolicyGeneration

PUT
/access-preview

CreateAccessPreview

GET
/analyzer

ListAnalyzers

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
The AWS Identity and Access Management Access Analyzer API provides a powerful, policy-as-code service that automatically identifies resources accessible from outside your AWS account or organization. At its core, the service continuously evaluates resource-based policies—such as Amazon S3 bucket policies, AWS Identity and Access Management (IAM) roles, Amazon KMS key policies, and AWS Lambda function policies—using logic-based reasoning to determine which resources grant access to unknown external principals. Its primary use case is for security and compliance teams within enterprises to proactively detect unintended data exposure, enforce least privilege principles, and audit cross-account and cross-service access. The API endpoints allow programmatic control to create, configure, and query analyzers, manage archive rules for storing findings, and generate custom policy documents, making it a foundational tool for automating cloud security posture management at scale.
🤖AI Agent Value
When exposed as tools through the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop or Cursor, the Access Analyzer API transforms from a cloud management tool into a dynamic, conversational security consultant for developers. The AI agent gains the ability to directly interact with the analyzer's intelligence layer, enabling a workflow where a developer can ask natural language questions like, "Analyze my S3 bucket named 'customer-data' for any public access risks," and the AI can orchestrate the appropriate API calls to fetch and interpret the latest findings. This integration significantly lowers the barrier to entry for complex security analysis, allowing developers without deep IAM expertise to get actionable insights within their IDE. The AI can also assist in policy remediation by using the policy generation endpoints to draft least-privilege policies based on the access patterns identified by the analyzer.
💬Example Workflows
Practical workflows enabled by this MCP server include continuous security auditing and automated policy refinement. A developer can instruct the AI agent to perform tasks such as: "Query all active analyzers and summarize the most critical high-severity findings from the last 24 hours," or "Create a new analyzer for my organization's member accounts and configure an archive rule to store resolved findings in this S3 bucket." The AI can further automate lifecycle management by saying, "Review the findings for IAM roles created by CloudFormation in the dev environment and use the policy generation tool to propose a tightened policy that only allows the necessary API actions based on observed usage." This creates a powerful feedback loop where the AI acts as an intermediary between the developer's intent and the service's analytical capabilities, enabling proactive security hardening and drift detection without manual console navigation.
🛡️Security & Auth
Critical security practices must be paramount when configuring this server. Although the API itself may use various authentication mechanisms, granting an AI agent access to these powerful tools requires strict adherence to the principle of least privilege. The IAM role or user credentials provided to the MCP server should have a minimal, scoped-down permission set, ideally restricted to read-only access to specific analyzer resources and the necessary findings reporting actions. Developers should avoid providing broad administrative permissions. It is essential to use managed policies or create custom policies that only allow actions like accessanalyzer:GetAnalyzer, accessanalyzer:ListFindings, and accessanalyzer:ListAnalyzers. Furthermore, sensitive analysis should be confined to designated accounts or regions, and all AI-agent-driven actions should be logged and monitored through AWS CloudTrail to maintain a clear audit trail of automated interactions with this critical security service.

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