Ai Core Mcp Server

AI Core MCP Server

An MCP (Model Context Protocol) server for SAP AI Core, powered by odata-mcp-proxy. It exposes SAP AI Core APIs as MCP tools, allowing AI assistants like Claude to manage your AI Core landscape through natural language.

The entire server is defined through a single JSON config file -- no custom code required.

How It Works

This project uses the odata-mcp-proxy npm package, which maps OData/REST services to MCP tools based on a configuration file. You provide a config describing your APIs and entity sets, and the proxy generates the corresponding MCP tools automatically.

AI Assistant (Claude, Cursor, etc.)
        |
        | MCP Protocol (HTTP or stdio)
        v
  odata-mcp-proxy
        |
        | REST + OAuth2 (via BTP Destination Service)
        v
  SAP AI Core APIs

Think of it like the SAP Application Router -- a ready-made runtime you configure, not code you write.

Exposed AI Core APIs

The config file (ai-core-api-config.json) defines two API groups:

Lifecycle Management (/v2/lm)

| Tool | Operations | Description | |------|-----------|-------------| | Scenarios | list, get | AI scenarios -- logical groupings of executables that define a ML use case | | ScenarioVersions | list | Versions of a scenario | | Executables | list, get | Workflow templates (training) and serving templates (inference) under a scenario | | Models | list | Models available in a scenario (e.g. LLMs in the generative AI hub) | | Configurations | list, get, create | Parameter sets binding a scenario/executable to inputs and hyperparameters | | Executions | list, get, create, update, delete | Training or batch inference runs, tracked through lifecycle states | | Deployments | list, get, create, update, delete | Running model-serving instances providing real-time inference endpoints | | Artifacts | list, get, create | Registered references to datasets, models, or files in an object store | | ExecutionSchedules | list, get, create, update, delete | Cron-based schedules that automatically create executions | | Metrics | list, delete | Training and evaluation metrics recorded during executions | | Meta | list | Runtime capabilities, supported features, and API version info | | DatasetFiles | get, create, delete | Upload, download, and delete files in the object store |

Administration (/v2/admin)

| Tool | Operations | Description | |------|-----------|-------------| | Repositories | list, get, create, update, delete | Onboarded git repos containing workflow/serving templates | | Applications | list, get, create, update, delete | ArgoCD applications that sync git repo content into AI Core | | DockerRegistrySecrets | list, create, update, delete | Credentials for pulling private container images | | ObjectStoreSecrets | list, create, update, delete | Credentials for S3, Azure Blob, GCS, or other object stores | | GenericSecrets | list, get, create, update, delete | Key-value secrets (API keys, tokens) for executions and deployments | | ResourceGroups | list, get, create, update, delete | Tenant isolation units that segregate AI assets and workloads | | Services | list, get | Service broker registrations exposing AI Core capabilities |

Prerequisites

• Node.js 18+ (20+ recommended)
• SAP BTP account with SAP AI Core provisioned
• BTP Destination configured for the AI Core API (AI_CORE) with OAuth2 authentication
• Cloud Foundry CLI (cf) and MBT Build Tool (mbt) for deployment

Project Structure

ai-core-mcp-server/
├── package.json                # Start script + odata-mcp-proxy dependency
├── ai-core-api-config.json     # API configuration (defines all MCP tools)
├── mta.yaml                    # BTP Cloud Foundry deployment descriptor
├── xs-security.json            # XSUAA OAuth2 configuration
├── default-env.json            # Local dev credentials (gitignored)
└── LICENSE

Getting Started

1. Install dependencies

npm install

2. Configure BTP destination

Create a BTP Destination pointing to the AI Core API:

| Destination | URL | |-------------|-----| | AI_CORE | https://api.ai.prod..aws.ml.hana.ondemand.com |

The destination should use OAuth2 client credentials authentication with the AI Core service key.

3. Local development

Create a default-env.json with your BTP service bindings (XSUAA, Destination, Connectivity) to run locally:

npm start

This runs odata-mcp-proxy --config ai-core-api-config.json.

4. Deploy to BTP

npm run build:btp     # Build MTA archive
npm run deploy:btp    # Deploy to Cloud Foundry

The MTA deployment provisions three service instances:

• Destination (lite) -- resolves API endpoints and manages OAuth2 tokens
• Connectivity (lite) -- enables secure backend connectivity
• XSUAA (application) -- handles OAuth2 authentication with role-based access control

Security

The XSUAA configuration (xs-security.json) defines three role templates:

| Role | Scopes | Description | |------|--------|-------------| | MCPViewer | read | Read-only access | | MCPEditor | read, write | Read and write access | | MCPAdmin | read, write, admin | Full administrative access |

OAuth2 redirect URIs are pre-configured for Claude.ai, Cursor, Microsoft Teams, and local development.

Creating Your Own MCP Server

This project demonstrates how easy it is to create a custom MCP server using odata-mcp-proxy. To build your own:

1. Create a new project and install the dependency:

``bash mkdir my-mcp-server && cd my-mcp-server npm init -y npm install odata-mcp-proxy ``

1. Add a start script to package.json:

``json { "scripts": { "start": "odata-mcp-proxy --config my-api-config.json" } } ``

1. Define your APIs in a config file (my-api-config.json):

``json { "server": { "name": "my-mcp-server", "version": "1.0.0", "description": "My custom MCP server" }, "apis": [ { "name": "my-api", "destination": "MY_BTP_DESTINATION", "pathPrefix": "/api/v1", "csrfProtected": true, "entitySets": [ { "entitySet": "Products", "description": "Product catalog", "category": "master-data", "keys": [{ "name": "Id", "type": "string" }], "operations": { "list": true, "get": true, "create": false, "update": false, "delete": false } } ] } ] } ``

1. Add your mta.yaml, xs-security.json, and BTP Destinations, then deploy. That's it -- no code to write.

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: lemaiwo
• Source: lemaiwo/ai-core-mcp-server
• License: MIT

Install and usage instructions live in the source repository linked above.