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Server

MCP server for AI-assisted development of CAP applications

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Scored 4 hours ago · breakdown

About Server

Server is an MCP server published by cap-js in the Developer Tools category: mCP server for AI-assisted development of CAP applications.

The repository has 116 stars and 19 forks, with the last commit yesterday. Recent commit activity is the strongest signal that reported issues will get attention.

Install

From source
git clone https://github.com/cap-js/mcp-server

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

Static checks derived from the repository 4 hours ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • ✓Tests presentA test suite exists in the repository.
  • ✓Dependencies pinnedA lockfile pins dependency versions.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

1 check has not been run yet.

Releases

v0.0.8v0.0.8 · 14 Oct 2026Added Accept precomputed `canonicalChunks` in `createEmbeddings`, validated as an array at creation time Export the default `MODEL` constant from `calculateEmbeddings` Changed Changed the default embedding model from…
v0.0.7v0.0.7 · 9 Oct 2026Added Hybrid search for `search_docs`: semantic cosine similarity combined with BM25 keyword search (SQLite FTS5, with a pure-JS fallback when FTS5 is not compiled into `node:sqlite`) via weighted Reciprocal Rank Fusion Optional local…
v0.0.6v0.0.6 · 21 Sep 2026Added Configurable embedding model per client via the `--model <name>` CLI flag and `CDS_MCP_MODEL` environment variable Capire-versioned embeddings with local manifest resolution Per-chunk metadata in `searchMarkdownDocs` output Changed…
v0.0.5v0.0.5 · 27 Apr 2026Added CLI argument management with `--offline` and `--download-embeddings`
v0.0.4v0.0.4 · 6 Mar 2026Added Support for MCP Bundles (Claude Desktop), Claude Code CLI plugin, and Claude Code MCP config Support for [MCP Registry](https://github.blog/ai-and-ml/generative-ai/how-to-find-install-and-manage-mcp-servers-with-the-github-mcp-registr…

README

Welcome to @cap-js/mcp-server

REUSE status

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

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 Claude Code

Register the server with Claude Code.

For all projects (user scope):

claude mcp add --scope user cds-mcp -- npx -y @cap-js/mcp-server

For the current project only (local scope, not committed):

claude mcp add cds-mcp -- npx -y @cap-js/mcp-server

For the current project only (committed to the repository):

claude mcp add --scope project cds-mcp -- npx -y @cap-js/mcp-server

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.json file 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 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

[!WARNING] When the package is installed globally, npx uses the installed version instead of downloading the latest from npm. You must update the package manually:

npm update -g @cap-js/mcp-server

This will provide the command cds-mcp, with which you can invoke the tools directly as follows.

# Search CAP documentation
cds-mcp search_docs "how to add columns to a select statement in CAP Node.js" 1

# Search for CDS model definitions
cds-mcp search_model . Books entity

Environment Variables

Variable Description
CDS_MCP_OFFLINE Set to true to use locally cached embeddings without checking for updates.
CDS_MCP_MODEL Embedding model name.
CDS_MCP_MANIFEST_TIMEOUT Manifest request timeout in positive integer milliseconds. Defaults to 15000.
CDS_MCP_DOWNLOAD_TIMEOUT Bundle response-header timeout in positive integer milliseconds. Defaults to 30000.

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_docs

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.

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".

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.

README fetched from the repository 4 hours ago. The original is authoritative.

Questions

About Server

How do I install Server?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Server safe to use with an AI agent?

Its trust score is 77 out of 100 (good). It passes 4 of the 5 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Server still maintained?

Yes — the latest release is v0.0.8 (14 Oct 2026), and the last commit was yesterday. The repository has 116 stars and 14 open issues.