AI Core MCP Server
An MCP server that exposes SAP AI Core's APIs as tools, defined entirely in one JSON config file for a proxy, so deployments, configurations and scenarios are managed through an assistant.
Managing an AI Core landscape — deployments, configurations, scenarios — through an assistant instead of the console, with the whole server expressed as configuration for the proxy rather than as code. The smallest of the three configurations, and the clearest illustration of what the engine underneath makes cheap.
Facts
Where it sits
Who is behind it
In the atlas: maintains 25, contributes to 39, wrote 12@lemaiwomaintainer
Before choosing this
Conditions
PracticalFunctional boundary
Configuration over an API you must already have entitled and provisioned; and the smallest of these repositories, so what it covers is what its config file happens to name.
PolicySAP API Policy 4.2026a§ 1.1, 1.2, 2.2.2from Agent access to your systemsAn MCP server uses SAP APIs to supply tools, resources or data to an agent.
- § 1.1 Published APIs. Published APIs are made available for the documented purposes identified by SAP, including integration, extensions and data exchange.
- § 1.2 Non-Published APIs. Customer and third-party applications must not access non-published APIs except where documentation permits it or SAP otherwise authorizes it.
- § 2.2.2 AI call sequences and data extraction. Outside SAP-endorsed architectures, data services or expressly identified pathways, the policy prohibits specified agentic API use and systematic or large-scale extraction.
MCP does not replace the API's documented-use boundary. Check that every endpoint is published for the intended use and whether the policy's AI-call or extraction provisions apply to the design.
SAP API Policy 4.2026a →What it declares
Every artifact
- Type
- MCP server
- Origin
- Independently maintained
Connections
Giving an AI agent access to the systems you run over MCP
Config-driven proxy turning OData and REST endpoints into MCP tools
Download
Architecture
Download
npm start runs the proxy binary against its JSON config, and there is no application code to run without it.README
quoted from lemaiwo/ai-core-mcp-server under MIT, readAI 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.<region>.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:
-
Create a new project and install the dependency:
mkdir my-mcp-server && cd my-mcp-server npm init -y npm install odata-mcp-proxy -
Add a start script to
package.json:{ "scripts": { "start": "odata-mcp-proxy --config my-api-config.json" } } -
Define your APIs in a config file (
my-api-config.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 } } ] } ] } -
Add your
mta.yaml,xs-security.json, and BTP Destinations, then deploy. That's it -- no code to write.
License
MIT
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Details
Sources
Sources are graph entities: the same page may back several entries, and an unreachable source flags every row that rests on it.
Compatibility and provenance
History in the atlas
- The concern it answers is reaching into an SAP system from outside, split out of MCP servers on this day; the sentence on the edge is unchanged.
- Triaged out of data/crawled/mcp-candidates.json, the sap-ai-mcp-servers sweep. Written from the repository's own README; the catalogue is credited as where it was found and its words are not reused.
- First seen.
For your agent
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