SAP AI Core Documentation MCP Server

A Model Context Protocol (MCP) server providing semantic search and intelligent access to SAP AI Core documentation.

Overview

This MCP server enables AI assistants like Claude to search, retrieve, and understand SAP AI Core documentation efficiently. It provides semantic search capabilities across the entire AI Core documentation repository from SAP-docs/sap-artificial-intelligence.

Features

• Semantic Search: Intelligent search across all SAP AI Core documentation
• Category Filtering: Search within specific areas (administration, development, integration, concepts)
• Document Retrieval: Get complete documentation pages with table of contents
• Topic-Specific Documentation: Quick access to documentation for specific AI Core topics
• Relevance Scoring: Results ranked by relevance to your query

Installation

Prerequisites

• Node.js 20.0.0 or higher
• npm or yarn

Quick Start

1. Clone this repository:

git clone <repository-url>
cd dlwr-dnl-ai-core-documentation-mcp

2. Install dependencies:

source ~/.zshrc && nvm use
npm install

3. Clone the SAP AI Core documentation as a git submodule:

git submodule add https://github.com/SAP-docs/sap-artificial-intelligence.git docs/sap-artificial-intelligence
git submodule update --init --recursive

4. Build the server:

npm run build

Configuration

Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "sap-ai-core-docs": {
      "command": "node",
      "args": [
        "/absolute/path/to/dlwr-dnl-ai-core-documentation-mcp/build/index.js"
      ]
    }
  }
}

Custom Documentation Path

To use a different documentation location:

{
  "mcpServers": {
    "sap-ai-core-docs": {
      "command": "node",
      "args": [
        "/absolute/path/to/dlwr-dnl-ai-core-documentation-mcp/build/index.js"
      ],
      "env": {
        "SAP_AI_CORE_DOCS_PATH": "/path/to/custom/docs"
      }
    }
  }
}

Available Tools

1. search_ai_core_docs

Semantically search SAP AI Core documentation.

Parameters:

• query (required): Search query string
• category (optional): Filter by category ('all', 'administration', 'development', 'integration', 'concepts')
• limit (optional): Maximum results (1-50, default: 10)

Example:

Search for "model training deployment best practices"

2. get_ai_core_document

Retrieve complete content of a specific documentation page.

Parameters:

• path (required): Relative path to document (from search results)

Example:

Get document at path "docs/sap-ai-core/getting-started.md"

3. get_ai_core_topic

Get comprehensive documentation for a specific SAP AI Core topic.

Parameters:

• topic_name (required): Name of the AI Core topic

Example:

Get documentation for "Model Training"

4. list_ai_core_categories

List all available documentation categories and top documents.

Example:

Show all available documentation categories

Development

Project Structure

dlwr-dnl-ai-core-documentation-mcp/
├── src/
│   ├── index.ts              # Entry point
│   ├── server.ts             # MCP server implementation
│   ├── types/
│   │   └── index.ts          # TypeScript type definitions
│   ├── indexer/
│   │   ├── markdown-parser.ts    # Markdown document parser
│   │   └── document-index.ts     # Document indexing & search
│   └── tools/
│       ├── search.ts             # Search tool implementation
│       ├── get-document.ts       # Document retrieval tool
│       ├── get-topic.ts          # Topic documentation tool
│       └── list-categories.ts    # Category listing tool
├── docs/
│   └── sap-artificial-intelligence/  # SAP AI Core docs (git submodule)
├── build/                     # Compiled JavaScript output
├── package.json
├── tsconfig.json
└── README.md

Build Commands

# Build once
npm run build

# Build and watch for changes
npm run watch

# Run the server directly
npm run dev

Testing

Test the server using the MCP Inspector:

npx @modelcontextprotocol/inspector node build/index.js

Architecture

Document Indexing

The server indexes all markdown files from the SAP AI Core documentation repository on startup:

1. Parsing: Uses unified and remark to parse markdown with frontmatter
2. Extraction: Extracts metadata, headings, sections, and keywords
3. Indexing: Creates a searchable index using Fuse.js for fuzzy semantic search
4. Categorization: Automatically categorizes documents based on folder structure

Search Strategy

• Multi-field search: Searches across titles, headings, content, and keywords
• Weighted scoring: Titles and keywords weighted higher than content
• Fuzzy matching: Handles typos and partial matches
• Context extraction: Returns relevant excerpts around matched terms

Use Cases

For delaware Netherlands Team

• AI Core Implementations: Quick access to AI Core documentation during client projects
• Training: Support for AI/ML enablement programs
• Solution Design: Research AI Core capabilities and best practices
• Troubleshooting: Find solutions for specific AI Core issues

For AI Agents (ConnectedBrain 2.0)

• Semantic Module: Integrate as a knowledge module in multi-agent orchestration
• Context Provider: Supply AI Core-specific context for solution generation
• Code Assistant: Help generate AI Core-compliant code and configurations

SAP AI Core Topics Covered

• Model Training: Training ML models using SAP AI Core
• Model Deployment: Deploying and serving models
• AI API: REST API for AI Core services
• Configuration Management: Managing AI Core configurations
• Resource Management: Managing compute resources and artifacts
• Integration: Integrating AI Core with SAP BTP services
• Security: Authentication, authorization, and data protection
• Monitoring: Logging, metrics, and observability

Performance

• Initial Index Build: ~5-10 seconds (depending on documentation size)
• Search Queries: <100ms (in-memory search)
• Memory Usage: ~50-100MB (indexed documents)

Roadmap

Phase 2 Enhancements

• Vector embeddings for improved semantic search
• Code sample extraction and indexing
• AI Core API pattern recognition
• Auto-update mechanism for documentation

Phase 3 Advanced Features

• Graph database for AI Core service relationships
• Context caching for frequently accessed docs
• Integration with SAP Help Portal
• Multi-language support

Contributing

This is a delaware Netherlands internal tool. For questions or contributions, contact the Data & AI team.

License

MIT License - Internal delaware Netherlands use

Support

For issues or questions:

• Internal: delaware Netherlands Data & AI team
• Documentation: SAP AI Core Official Docs
• GitHub: SAP AI Core Documentation Repository

---

Built with ❤️ by delaware Netherlands Data & AI Team

Part of our "platform-first, cloud-native" AI-empowered operations initiative