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Serve your application over MCPSAP and community
Model Context Protocol Adapter — CAP guideModel Context Protocol Adapter — CAP guideDocuments @cap-js/mcp and com.sap.cds:cds-adapter-mcp, service annotations, generated describe/query/call tools and local client setup. The guide records Java doc-comment configuration, unbound-action-based data changes, incomplete XSUAA PKCE support and the adapter’s governance boundaries.
CachingSAP and community
CAP just got faster: What's new in cds-caching 1.0CAP just got faster: What's new in cds-caching 1.0What changed in cds-caching at 1.0, announced at reCAP 2025 by the plugin's own maintainer. The substantive change is the read-through API: cache.rt.run(), send(), exec() and wrap() replace the bare cache.run() and cache.send(), and each returns the cache key and hit-or-miss metadata beside the result rather than the value alone. The second is quieter and easier to miss on an upgrade: user, tenant and locale stopped being folded into request-based cache keys by default and became opt-in through keyManagement, or per operation through a key template. Both of those still describe 3.x. What does not is the second half of the post: the metrics configuration it shows was reshaped at 2.0 and the statistics and dashboard settings were removed at 3.0, so take the monitoring and dashboard sections from the README instead. Read it as the successor to the introduction post, which still shows the deprecated calls.published 2025-07-15
CAP just got faster: What's new in cds-caching 1.0
cds-caching 1.0, now 3.1.1, cds 9, maintenance until 2027-06. Affects A reference.
LLM integrationSAP and community
Data MaskingData MaskingData masking replaces identifiable or confidential text in a prompt with placeholders such as <Person_0> or <Organization_1> before it reaches a large language model, using pattern matching and machine learning to find what to replace. The page states SAP AI Core supports it as a feature and sets out five steps for applying it, defining the masking scope, choosing a technique, preserving the prompt's intent, checking compliance and risk, then testing and monitoring the result. That is the shape data masking takes on the BTP door.
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.
Prompt TemplatingPrompt TemplatingPrompt templating replaces a static prompt with a structured, reusable template whose placeholders are filled in at runtime, which the page says helps LLM responses stay consistent and lets the same instructions be adapted across tasks. On SAP AI Core this is implemented through the Prompt Registry, and the page states that storing prompts as plain strings in code is possible but not recommended. That is prompt management as a stored and managed object rather than a string embedded in the caller.
RAG on CDSSAP and community
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.