Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Provides curated documentation access via the Gemini API, enabling users to query and interact with technical docs effectively by overcoming context and search limitations.
Exposes an internal engineering knowledge base to AI assistants, allowing users to search and retrieve standards, runbooks, and architecture decisions. It supports RAG-enhanced search, document scraping, and specialized prompts for incident investigation and code reviews.
Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
Provides RAG (Retrieval Augmented Generation) access to technical documentation through MCP, enabling LLMs to search and retrieve relevant documentation on-demand.
Enables semantic search over Databricks docs, API reference, Terraform provider docs, and knowledge base via tools like search_databricks_docs and research.