This implementation is based on the Model Context Protocol (MCP) put forward by Anthropic. For more information on MCP, please have a look at their official documentation.
🔧 In active development - 1.0 release scheduled for Summer 2025
A CAP (Cloud Application Programming) plugin that automatically generates Model Context Protocol (MCP) servers from your CAP services using simple annotations. Transform your CAP OData services into AI-accessible resources, tools, and prompts with minimal configuration.
The Model Context Protocol bridges the gap between your enterprise data and AI agents. By integrating MCP with your CAP applications, you unlock:
- AI-Native Data Access: Your CAP services become directly accessible to AI agents like Claude, enabling natural language queries against your business data
- Enterprise Integration: Seamlessly connect AI tools to your SAP systems, databases, and business logic
- Intelligent Automation: Enable AI agents to perform complex business operations by combining multiple CAP service calls
- Developer Productivity: Allow AI assistants to help developers understand, query, and work with your CAP data models
- Business Intelligence: Transform your structured business data into AI-queryable resources for insights and analysis
This plugin is currently in active development (v0.9.0) and is not ready for production use. APIs and annotations may change in future releases. Use in development and testing environments only.
Version 1.0 of the plugin is planned to release shortly after auth integration is complete.
❗Currently there is a bug in the
@modelcontextprotocol/sdkpackage that breaks the RFC template strings. The issue has been reported. Until this issue is fixed, the resource reads for dynamic queries are only possible if all query options are provided.
npm install @gavdi/cap-mcpThe plugin follows CAP's standard plugin architecture and will automatically integrate with your CAP application.
This plugin transforms your annotated CAP services into a fully functional MCP server that can be consumed by any MCP-compatible AI client.
- 📊 Resources: Expose CAP entities as MCP resources with OData v4 query capabilities
- 🔧 Tools: Convert CAP functions and actions into executable MCP tools
- 💡 Prompts: Define reusable prompt templates for AI interactions
- 🔄 Auto-generation: Automatically creates MCP server endpoints based on annotations
- ⚙️ Flexible Configuration: Support for custom parameter sets and descriptions
Transform CAP entities into AI-queryable resources:
service CatalogService {
@readonly
@mcp: {
name : 'books',
description: 'Book data list',
resource : [
'filter',
'orderby',
'select',
'skip',
'top'
]
}
entity Books as projection on my.Books;
// Enable all OData query options
@mcp: {
name : 'authors',
description: 'Author data list',
resource : true
}
entity Authors as projection on my.Authors;
// Or maybe you just want it as a static top 100 list of data?
@mcp: {
name : 'genres',
description: 'Book genre list',
resource : []
}
entity Genres as projection on my.Genres;
}Generated MCP Resource Capabilities:
- OData v4 Query Support:
$filter,$orderby,$top,$skip,$select - Natural Language Queries: "Find books by Stephen King with stock > 20"
- Dynamic Filtering: Complex filter expressions using OData syntax
- Flexible Selection: Choose specific fields and sort orders
Convert CAP functions and actions into executable AI tools:
// Service-level function
@mcp: {
name : 'get-author',
description: 'Gets the desired author',
tool : true
}
function getAuthor(input: String) returns String;
// Entity-level action
extend projection Books with actions {
@mcp: {
name : 'get-stock',
description: 'Retrieves stock from a given book',
tool : true
}
function getStock() returns Integer;
}Define reusable AI prompt templates:
annotate CatalogService with @mcp.prompts: [{
name : 'give-me-book-abstract',
title : 'Book Abstract',
description: 'Gives an abstract of a book based on the title',
template : 'Search the internet and give me an abstract of the book {{book-id}}',
role : 'user',
inputs : [{
key : 'book-id',
type: 'String'
}]
}];The plugin automatically:
- Scans your CAP service definitions for
@mcpannotations - Generates appropriate MCP resources, tools, and prompts
- Creates ResourceTemplates with proper OData v4 query parameter support
Once configured, AI agents can interact with your CAP data naturally; Let's take an example from the standard CAP Bookshop:
- "Show me the top 5 books with highest stock" → Queries Books resource with
$orderby=stock desc&$top=5 - "Find authors whose names contain 'Smith'" → Uses
$filter=contains(name,'Smith')on Authors resource - "Get the current stock for book ID 123" → Calls the
get-stocktool for the specified book - "Give me a book recommendation" → Executes the
book-recommendationtool
While this shows how this example CDS annotation works, the possibilities are endless and only you and your data sets the boundaries.
Your CAP service includes a workflow management system with MCP integration:
service WorkflowService {
@mcp: {
name : 'get-my-pending-approval',
description: 'Fetches workflows awaiting approval by the specified user',
tool : true
}
function getPendingApproval(userId: String) returns array of Workflows;
}1. User Query
User: "Hey <Agent>, do I have any workflows pending approval?"
2. AI Agent Processing
- Agent recognizes this as a request for pending approval information
- Identifies the
get-my-pending-approvaltool as the appropriate method - Determines the user's ID from context (session, authentication, etc.)
3. MCP Tool Execution
// Agent calls the MCP tool
{
"tool": "get-my-pending-approval",
"arguments": {
"userId": "john.doe@company.com"
}
}4. CAP Service Processing
- Your CAP service receives the tool call
- Executes
getPendingApproval("john.doe@company.com") - Queries your workflow database/system
- Returns structured workflow data
5. AI Response
Agent: "You have 3 workflows pending your approval:
• **Purchase Order #PO-2024-001**
Submitted by: Sarah Johnson
Amount: $12,500
Submitted: 2 days ago
• **Budget Request - Marketing Q2**
Submitted by: Mike Chen
Amount: $45,000
Submitted: 1 day ago
• **New Employee Onboarding - Jane Smith**
Submitted by: HR Department
Start Date: Next Monday
Submitted: 4 hours ago
Would you like me to help you review any of these in detail?"
- Instant Access: No need to log into workflow systems or navigate complex UIs
- Contextual Intelligence: AI can prioritize based on urgency, amounts, or business rules
- Natural Interaction: Users can ask follow-up questions in plain language
- Integration Ready: Works with existing CAP-based workflow systems
- Mobile Friendly: Access approvals from any MCP-compatible AI client
If you want to test the MCP implementation you have made on your CAP application locally, you have 2 options available (that does not involve direct integration with AI Agent).
You can inspect the MCP implementation by utilizing the official @modelcontextprotocol/inspector.
This inspector can be started up through either the included npm run inspect command, or by running npx @modelcontextprotocol/inspector.
For plugin implementation implementation in your own project it is recommended to add the above command to your own script collection.
For more information on the inspector, please see the official documentation.
This repository comes with a Bruno collection available that includes some example queries you can use to verify your MCP implementation. These can be found in the bruno directory.
Contributions are welcome! This is an open-source project aimed at bridging CAP applications with the AI ecosystem.
- Issues: Report bugs and request features
- Pull Requests: Submit improvements and fixes
- Documentation: Help improve examples and guides
- Testing: Share your use cases and edge cases
This project is licensed under the Apache-2.0 License - see the LICENSE.md file for details.
(c) Copyright by Gavdi Labs 2025 - All Rights Reserved
Transform your CAP applications into AI-ready systems with the power of the Model Context Protocol.