CDK API MCP Server

The CDK API MCP Server is a specialized Model Context Protocol (MCP) server that exposes the full breadth of AWS Cloud Development Kit (CDK) documentation directly to AI assistants. By packaging every module, construct, and source file from the and the newer alpha namespace into a single, queryable resource set, developers can let Claude or other AI tools fetch up‑to‑date API references on demand. This eliminates the need for manual browsing or local copies of the CDK docs, ensuring that AI‑powered workflows always reference the latest library version.

What Problem Does It Solve?

Working with CDK involves navigating a large, constantly evolving set of constructs and patterns. Developers often spend valuable time searching through GitHub repositories or the official CDK website for method signatures, property definitions, and usage examples. The MCP server turns this manual lookup into an automated query: an AI assistant can ask for the constructor signature of or retrieve the README of a specific construct, and the server will return the exact text from the bundled documentation. This reduces context switching, speeds up onboarding for new team members, and keeps code generation tools grounded in the real API surface.

Core Value to AI‑Enabled Development

For teams integrating AI assistants into their CI/CD pipelines, IDE extensions, or knowledge bases, the CDK API MCP Server acts as a lightweight, self‑contained knowledge source. Because all documentation files are packaged into the Python artifact, the server can run offline—an essential feature for regulated environments or internal tooling. The server’s resource schema follows a clear URI pattern (), allowing AI clients to discover modules with and drill down into files with . This structured access lets AI models provide precise, snippet‑level answers rather than generic suggestions.

Key Features

• Complete CDK Coverage: Includes both stable modules and alpha constructs, ensuring developers have access to the latest experimental features.
• Static Resource Registry: Exposes modules as static resources that can be enumerated, enabling AI agents to present a navigable list of available constructs.
• File‑Level Retrieval: Clients can request individual files—such as or README documentation—by URI, giving AI assistants the exact context needed for accurate code generation.
• Offline‑Ready Package: All documentation is bundled into the Python distribution, so no external network calls are required once installed.
• MCP Compatibility: Adheres to the standard MCP interface, making it plug‑and‑play with any AI system that supports resource discovery and reading.

Use Cases & Real‑World Scenarios

• Rapid Prototyping: A developer asks the AI assistant to “show me how to set up an S3 bucket with versioning” and receives the exact CDK snippet from the official docs, cutting down trial‑and‑error time.
• Onboarding: New hires can query the AI for construction patterns without digging through GitHub, accelerating their learning curve.
• Code Review Automation: CI pipelines can invoke the MCP server to fetch construct definitions and validate that custom code adheres to best practices.
• Documentation Generation: Tools can pull the latest API docs into internal wikis or knowledge bases automatically, ensuring consistency across teams.

Unique Advantages

Unlike generic web‑scraping solutions, the CDK API MCP Server provides a structured, versioned view of the documentation. It guarantees that every URI maps to a deterministic file snapshot, eliminating the risk of stale or broken links. Its tight integration with MCP means that AI assistants can treat CDK docs as first‑class resources, just like other APIs or data sources they already consume. This seamless experience turns CDK knowledge into an AI‑friendly asset, empowering developers to write correct infrastructure code faster and with fewer errors.