io.github.cap-js/mcp-server MCP Server
io.github.cap-js/mcp-server
AI-assisted development for SAP CAP applications with semantic search of models and documentation.
What is the io.github.cap-js/mcp-server MCP server?
The @cap-js/mcp-server is a Model Context Protocol server for the SAP Cloud Application Programming Model (CAP) that enables AI-assisted development of CAP applications. It provides tools to search CDS model definitions and CAP documentation, helping AI models understand project structure, entities, relationships, and API usage patterns.
This MCP server integrates with AI coding assistants (Claude, Cursor, GitHub Copilot, etc.) to provide context-aware help for CAP development. It uses fuzzy search to find CDS definitions like entities and services, and semantic search with local embeddings to find relevant CAP documentation—all without leaving your machine.
How to install io.github.cap-js/mcp-server
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
search_model— Performs fuzzy searches against CDS model definitions (entities, fields, services, HTTP endpoints) from compiled .cds files to find definitions and their relationships.search_docs— Uses vector embeddings to semantically search CAP documentation locally, finding relevant docs even when queries don't use exact keywords.
Use cases
- Understand which CDS services exist in a CAP project and where they are served
- Discover entity relationships and field definitions in your data model
- Get guidance on CAP Node.js APIs and syntax when modifying CDS models or select statements
- Search CAP documentation without leaving your IDE while coding
- Enable agentic coding workflows where AI agents can autonomously explore and modify CAP applications
io.github.cap-js/mcp-server MCP server FAQ
It's an MCP server that connects AI coding assistants to SAP CAP projects, providing semantic search over your CDS models and CAP documentation to enable AI-assisted development.
Yes, it's open-source software licensed under the REUSE standard. You can install it via npm.
Run `claude mcp add cds-mcp -- npx -y @cap-js/mcp-server` for Claude Code, or configure it in your MCP client's settings with the command `npx -y @cap-js/mcp-server`.
No, it runs locally on your machine and does not require external API keys or authentication.
It searches your local CDS model definitions (entities, services, endpoints) and includes preprocessed CAP documentation with semantic embeddings for intelligent search.
Yes, install globally with `npm i -g @cap-js/mcp-server` and use commands like `cds-mcp search_model . Books entity` or `cds-mcp search_docs "query" 1`.
README (reference)
Source of truth, from the repository.
Welcome to @cap-js/mcp-server
About This Project
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?
Table of Contents
- About This Project
- Requirements
- Setup
- Available Tools
- Support, Feedback, Contributing
- Security / Disclosure
- Code of Conduct
- Licensing
- Acknowledgments
Requirements
See Getting Started on how to jumpstart your development and grow as you go with SAP Cloud Application Programming Model.
Setup
Configure your MCP client (Cline, opencode, Claude Code, GitHub Copilot, etc.) to start the server using the command npx -y @cap-js/mcp-server as in the following examples.
Usage in VS Code
Example for VS Code extension Cline:
{
"mcpServers": {
"cds-mcp": {
"command": "npx",
"args": ["-y", "@cap-js/mcp-server"],
"env": {}
}
}
}
Example for VS Code global mcp.json:
Note: GitHub Copilot uses the
mcp.jsonfile as source for it's Agent mode.
{
"servers": {
"cds-mcp": {
"command": "npx",
"args": ["-y", "@cap-js/mcp-server"],
"env": {},
"type": "stdio"
},
"inputs": []
}
}
See VS Code Marketplace for more agent extensions.
Usage in Claude Code
Register the server with Claude Code:
claude mcp add cds-mcp -- npx -y @cap-js/mcp-server
Usage in OpenAI Codex
Register the server with OpenAI Codex:
codex mcp add cds-mcp -- npx -y @cap-js/mcp-server
Usage in opencode
Example for opencode:
{
"mcp": {
"cds-mcp": {
"type": "local",
"command": ["npx", "-y", "@cap-js/mcp-server"],
"enabled": true
}
}
}
Rules
The following rules help the LLM use the server correctly:
- 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 `cds` 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).
CLI Usage
You can also use the tools directly from the command line.
npm i -g @cap-js/mcp-server
This will provide the command cds-mcp, with which you can invoke the tools directly as follows.
# Search for CDS model definitions
cds-mcp search_model . Books entity
# Search CAP documentation
cds-mcp search_docs "how to add columns to a select statement in CAP Node.js" 1
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:
search_model
This tool performs fuzzy searches against names of definitions from the compiled CDS model (Core Schema Notation).
CDS compiles all your .cds files into a unified model representation that includes:
- All definitions and their relationships
- Annotations
- HTTP endpoints
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".
search_docs
This tool uses vector embeddings to locally search through preprocessed CAP documentation, stored as embeddings. The process works as follows:
- Query processing: Your search query is converted to an embedding vector.
- 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.
Support, Feedback, Contributing
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.
Security / Disclosure
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.
Code of Conduct
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.
Licensing
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.
Acknowledgments
- 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.
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