SAP AI Core Documentation MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SAP_AI_CORE_DOCS_PATH | No | Custom path to the documentation location. If not provided, the server typically looks for the documentation in the default 'docs/sap-artificial-intelligence' directory within the project. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_ai_core_docsA | Semantically search SAP AI Core documentation. Returns relevant documents with excerpts and relevance scores. |
| get_ai_core_documentB | Get the complete content of a specific AI Core documentation page by its path. |
| get_ai_core_topicC | Get comprehensive documentation for a specific SAP AI Core topic (e.g., "Model Training", "Deployment", "AI API", "Configuration"). |
| list_ai_core_categoriesB | List all available documentation categories and their contents. Useful for exploring what documentation is available. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
The tools have mostly distinct purposes: get_ai_core_document retrieves a specific page by path, get_ai_core_topic fetches comprehensive topic documentation, list_ai_core_categories lists categories for exploration, and search_ai_core_docs performs semantic searches. However, get_ai_core_document and get_ai_core_topic could be slightly confusing as both retrieve documentation content, though they differ in input specificity (path vs. topic).
All tools follow a consistent verb_noun pattern with 'ai_core' as a prefix: get_ai_core_document, get_ai_core_topic, list_ai_core_categories, and search_ai_core_docs. This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.
Four tools are reasonable for a documentation server, covering key operations like retrieval, listing, and searching. It is slightly thin but well-scoped for the domain, as it includes essential functions without unnecessary bloat. A few more tools (e.g., for metadata or filtering) could enhance it, but it's not a significant gap.
The tool set covers core documentation workflows: browsing (list_ai_core_categories), retrieving by path or topic (get_ai_core_document, get_ai_core_topic), and searching (search_ai_core_docs). Minor gaps exist, such as no tools for updating documentation or handling user-specific queries, but agents can work around these with the provided tools for most use cases.