distillery for AI Search

distillery: MCP server providing searchable access to technical documentation

distillery, developed by Norrietaylor, is an MCP server that gives AI assistants searchable access to technical documentation. The server lets MCP-compatible clients query indexed manuals, API references, and project guides so models effectively retrieve context during development and research tasks. Key capabilities center on documentation indexing and intent-based retrieval, plus local-hosted search for private corpora. The tool targets software developers, technical writers, and AI practitioners who need model-aware access to project-specific documentation.

It acts as a protocol bridge rather than a standalone search engine

Distillery runs as a Model Context Protocol server that supplies indexed documentation to other AI applications; it is not a standalone search engine. That design supports retrieval-augmented generation workflows where a model queries an external context provider. Typical indexed sources are:

• manuals
• API references
• project guides

Search behavior depends on intent matching and source quality

The server performs intent-focused retrieval rather than literal keyword lookup, which helps surface passages aligned with developer queries. That semantic approach improves context-aware model responses when documentation is thorough and well structured. Retrieval relevance falls when source material is sparse or poorly formatted. Because the project is open source, teams can audit the implementation and adapt parsing or indexing behavior to better match their documentation style.

Deployment fits engineering workflows and preserves local control

Distillery runs on a Node.js environment and is written in TypeScript, making it inspectable and extendable by engineering teams. It operates across desktop platforms where Node.js is supported and exposes a command-line interface for scripted index and configuration tasks. Local indexing keeps sensitive files on the host machine rather than forcing cloud upload, reflecting the project's privacy-focused design and suitability for private documentation stores.

Practical for MCP teams, but requires an existing integration workflow

Distillery is a practical option for development teams and technical writers who already use MCP-compatible clients and need model-accessible documentation. It requires an existing MCP workflow and some engineering setup to connect the server into client configurations, so teams outside that ecosystem find limited immediate benefit. Plan time for customization and index tuning during onboarding to align retrieval with documentation structure.