Model Context Protocol (MCP) server for AI-assisted development of CAP applications.
npm install @cap-js/mcp-server
> [!NOTE]
> This project is in alpha state.
A Model Context Protocol (MCP) server for the SAP Cloud Application Programming Model (CAP).
Use it for AI-assisted development of CAP applications (_agentic coding_).
The server helps AI models answer questions such as:
- _Which CDS services are in this project, and where are they served?_
- _What are the entities about and how do they relate?_
- _How do I add columns to a select statement in CAP Node.js?_
- About This Project
- Requirements
- Setup
- Usage in VS Code
- Usage in opencode
- CLI Usage
- Available Tools
- search_model
- search_docs
- Support, Feedback, Contributing
- Security / Disclosure
- Code of Conduct
- Licensing
- Acknowledgments
See Getting Started on how to jumpstart your development and grow as you go with SAP Cloud Application Programming Model.
``sh`
npm i -g @cap-js/mcp-server
This will provide the command cds-mcp to start the CAP MCP server.
Configure your MCP client (Cline, opencode, Claude Code, GitHub Copilot, etc.) to start the server using the cds-mcp command.
Example for VS Code extension Cline:
`json`
{
"mcpServers": {
"cds-mcp": {
"command": "cds-mcp",
"args": [],
"env": {}
}
}
}
See VS Code Marketplace for more agent extensions.
Example for opencode:
`json`
{
"mcp": {
"cds-mcp": {
"type": "local",
"command": ["cds-mcp"],
"enabled": true
}
}
}
The following rules help the LLM use the server correctly:
`markdowncds
- You MUST search for CDS definitions, like entities, fields and services (which include HTTP endpoints) with cds-mcp, only if it fails you MAY read \*.cds files in the project.
- You MUST search for CAP docs with cds-mcp EVERY TIME you create, modify CDS models or when using APIs or the CLI from CAP. Do NOT propose, suggest or make any changes without first checking it.`
Add these rules to your existing global or project-specific AGENTS.md (specifics may vary based on respective MCP client).
For experimental purposes, you can also use the tools directly from the command line:
`shSearch for CDS model definitions
cds-mcp search_model . Books entity
Available Tools
> [!NOTE]
> Tools are meant to be used by AI models and do not constitute a stable API.
The server provides these tools for CAP development:
$3
This tool performs fuzzy searches against names of definitions from the compiled CDS model (Core Schema Notation).
CDS compiles all your
The fuzzy search algorithm matches definition names and allows for partial matches, making it easy to find entities like "Books" even when searching for "book".
This tool uses vector embeddings to locally search through preprocessed CAP documentation, stored as embeddings. The process works as follows:
1. Query processing: Your search query is converted to an embedding vector.
2. Similarity search: The system finds documentation chunks with the highest semantic similarity to your query.
This semantic search approach enables you to find relevant documentation even when your query does not use the exact keywords found in the docs, all locally on your machine.
This project is open to feature requests/suggestions, bug reports, and so on, via GitHub issues. Contribution and feedback are encouraged and always welcome. For more information about how to contribute, the project structure, as well as additional contribution information, see our Contribution Guidelines.
If you find any bug that may be a security problem, please follow our instructions at in our security policy on how to report it. Please don't create GitHub issues for security-related doubts or problems.
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone. By participating in this project, you agree to abide by its Code of Conduct at all times.
Copyright 2025 SAP SE or an SAP affiliate company and @cap-js/cds-mcp contributors. Please see our LICENSE for copyright and license information. Detailed information including third-party components and their licensing/copyright information is available via the REUSE tool.
- onnxruntime-web is used for creating embeddings locally.
- @huggingface/transformers.js is used to compare the output of the WordPiece tokenizer.
- @modelcontextprotocol/sdk provides the SDK for MCP.