Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@nanograph-mcp search for 'machine learning' in my datasources"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Build Nanograph datasources from Markdown or PDF corpora and serve them through MCP or a small HTTP/JSON wrapper.
Ingest commands: nanograph-ingest seed --config ..., build, and smoke
Server commands: nanograph-mcp --registry ... and nanograph-http --registry ...
MCP tools: sources, source, documents, document, sections, search, passage
HTTP routes: GET /healthz, GET /datasources, GET /datasources/{name},
GET /datasources/{name}/documents,
GET /datasources/{name}/documents/{document_id},
GET /datasources/{name}/documents/{document_id}/sections, POST /search,
POST /passage, and POST /mcp
Use /healthz for health checks. Other application responses use the same
schema-versioned envelope.
Search modes:
mode = "keyword" for all datasources
mode = "search" only for datasources built with embeddings enabled
Related MCP server: md-graph
nanograph-ingest smoke --config examples/corpus-markdown.toml
nanograph-http --registry examples/datasources-public.toml --host 127.0.0.1 --port 8765The public registry example uses only home, exposure, and enabled for
each datasource. Host-local path overrides such as database and config are
outside the documented v1 contract.
Documentation:
This server cannot be deployed
Serve a folder of Markdown notes as an MCP server: hybrid search, reading, and sourced answers.
Document-to-Markdown MCP server — convert PDF, Office and HTML into LLM-ready Markdown.
Personal knowledge base MCP server with semantic search, auto-categorization, metadata extraction
Knowledge coverage map and health score. Ingest docs into a governed knowledge graph via MCP.