Provides direct access to local documentation files through simple search and overview tools, enabling LLMs to query project-specific markdown documentation without requiring vector databases or RAG pipelines.
Enables AI models to seamlessly access and query local markdown technical documentation files, providing automatic documentation context without explicit prompting.
Enables users to document data and connect it to AI agents by defining tools and instructions in markdown files. It supports building RAG and text-to-SQL applications that can be deployed as MCP servers, APIs, or CLIs.
Provides deterministic, read-only access to engineering decisions, requirements, and designs stored as Markdown in a repository, enabling agents to retrieve and follow decided context without embeddings or external services.
