A local knowledge navigation server that exposes structured documentation to AI agents through the Model Context Protocol (MCP).
View on GitHub ↗A local knowledge navigation server that exposes structured documentation to AI agents through the Model Context Protocol (MCP).
Structured Knowledge Filesystem is designed for documentation that already has a meaningful hierarchy, such as product docs, engineering guides, standard operating procedures, and Git-managed knowledge bases. It preserves that human-curated structure and gives an agent three focused, read-only capabilities:
- listdirectory: browse the knowledge tree; - search: run deterministic searches across Markdown, MDX, and text files; - readfile: read the source document after its location has been confirmed.
The intended workflow is browse → search → read. The agent does not need to guess paths, and the answer can include a traceable source file.
Many knowledge systems immediately flatten documents into chunks and vector indexes. This project takes a different approach when the hierarchy itself carries meaning:
- no database or vector index is required; - documents remain local and are not uploaded by the server; - search results include repository-relative paths, line numbers, and snippets; - the server is a small Go binary with a narrow read-only surface; - the same workflow works with a local Git checkout or a curated documentation folder.
The repository includes example-knowledge/, a multi-level Markdown knowledge base containing three fictional products:
- Product Alpha: order management and product catalog; - Product Beta: customer support and identity verification; - Product Gamma: analytics and data pipelines.
From the project README.
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