AI Core Server
БесплатноНе проверенExposes SAP AI Core APIs as MCP tools, enabling AI assistants to manage AI Core lifecycle and administration through natural language.
Описание
Exposes SAP AI Core APIs as MCP tools, enabling AI assistants to manage AI Core lifecycle and administration through natural language.
README
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-proxyAdd 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
Установка AI Core Server
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/lemaiwo/ai-core-mcp-serverFAQ
AI Core Server MCP бесплатный?
Да, AI Core Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для AI Core Server?
Нет, AI Core Server работает без API-ключей и переменных окружения.
AI Core Server — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить AI Core Server в Claude Desktop, Claude Code или Cursor?
Открой AI Core Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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