Cloudscape Docs MCP Server

A Model Context Protocol (MCP) server that provides semantic search over AWS Cloudscape Design System documentation. Built for AI agents and coding assistants to efficiently query component documentation.

Features

Semantic Search - Find relevant documentation using natural language queries powered by Jina Code Embeddings 0.5B model
Token Efficient - Returns concise file lists first, full content on demand
Hardware Optimized - Automatic detection of Apple Silicon (MPS), CUDA, or CPU
Local Vector Store - Uses LanceDB for fast, file-based vector search

Transport

This server uses the MCP stdio transport protocol.
Streamable HTTP transport coming soon.

Tools

Tool	Description
`cloudscape_search_docs`	Search the documentation index. Returns top 5 relevant files with titles and paths.
`cloudscape_read_doc`	Read the full content of a specific documentation file.

Requirements

Python 3.13+
~3GB disk space for the embedding model
8GB+ RAM recommended

Installation

# Clone the repository git clone https://github.com/praveenc/cloudscape-docs-mcp.git cd cloudscape-docs-mcp # Create virtual environment and install dependencies uv sync # Or with pip pip install -e .

Setup

1. Add Documentation

Place your Cloudscape documentation files in the docs/ directory. Supported formats:

.md (Markdown)
.txt (Plain text)
.tsx / .ts (TypeScript/React)

2. Build the Index

Run the ingestion script to create the vector database:

uv run ingest.py

This will:

Scan all files in docs/
Chunk content into ~2000 character segments
Generate embeddings using Jina Code Embeddings 0.5B embedding model
Store vectors in data/lancedb/

Note: Running uv run ingest.py multiple times is safe but performs a full re-index each time. The script uses mode="overwrite" which drops and recreates the database table. There is no incremental update or change detection—all documents are re-scanned and re-embedded on every run. This is idempotent (same docs produce the same result) but computationally expensive for large documentation sets.

3. Run the Server

uv run server.py

MCP Client Configuration

Claude Desktop

Add to your mcp.json:

{ "mcpServers": { "cloudscape-docs": { "command": "uv", "args": ["run", "--directory", "/path/to/cloudscape-docs-mcp", "python", "server.py"] } } }

Cursor / VS Code / Windsurf / Kiro

Add to your MCP settings:

{ "cloudscape-docs": { "command": "uv", "args": ["run", "--directory", "/path/to/cloudscape-docs-mcp", "python", "server.py"] } }

Zed

Add to your Zed settings (settings.json):

{ "context_servers": { "cloudscape-docs": { "command": { "path": "uv", "args": ["run", "--directory", "/path/to/cloudscape-docs-mcp", "python", "server.py"] } } } }

Usage Example

Once connected, an AI assistant can:

Search for components:
User: "How do I use the Table component with sorting?" Agent: [calls cloudscape_search_docs("table sorting")]
Read specific documentation:
Agent: [calls cloudscape_read_doc("docs/components/table/sorting.md")]

Project Structure

cloudscape-docs-mcp/ ├── server.py # MCP server with search/read tools ├── ingest.py # Documentation indexing script ├── pyproject.toml # Project dependencies ├── docs/ # Documentation files (partially curated) │ ├── components/ # Component documentation │ ├── foundations/ # Design foundations │ └── genai_patterns/# GenAI UI patterns └── data/ # Generated vector database (gitignored) └── lancedb/

Configuration

Key settings in server.py and ingest.py:

Variable	Default	Description
`MODEL_NAME`	`jinaai/jina-code-embeddings-0.5b`	Embedding model
`VECTOR_DIM`	`1536`	Vector dimensions
`MAX_UNIQUE_RESULTS`	`5`	Max search results returned
`DOCS_DIR`	`./docs`	Documentation source directory
`DB_URI`	`./data/lancedb`	Vector database location

Development

# Install dev dependencies uv sync --group dev # Run with MCP inspector npx @modelcontextprotocol/inspector uv --directory /path/to/cloudscape_docs run server.py # Alternatively, use mcp cli to launch the server mcp dev server.py

License

MIT License - See LICENSE for details.

Acknowledgments

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Related Resources

GitHub Repository

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Cloudscape Docs MCP Server

Cloudscape Docs MCP Server

Features

Transport

Tools

Requirements

Installation

Setup

1. Add Documentation

2. Build the Index

3. Run the Server

MCP Client Configuration

Claude Desktop

Cursor / VS Code / Windsurf / Kiro

Zed

Usage Example

Project Structure

Configuration

Development

License

Acknowledgments

Related Resources

New MCP Servers

MCP directory API