MCP Server

Hyper-Extract ships an MCP server so MCP-capable assistants (Claude Desktop, IDE agents, etc.) can query and export your Knowledge Abstracts over the Model Context Protocol.

It is read + export only — it never creates, mutates, or deletes a KA.

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Install & Run

pip install 'hyperextract[mcp]'

# start the server (stdio transport)
he-mcp
# equivalent:
python -m hyperextract.mcp_server

The server reads your LLM/embedder configuration from ~/.he/config.toml — the same config the CLI uses, so run he config init ... first (see Configuration).

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Connect it to an MCP client

Point your MCP client at the he-mcp command. For a Claude Desktop–style config:

{
  "mcpServers": {
    "hyper-extract": {
      "command": "he-mcp"
    }
  }
}

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Tools

Tool	Description	Needs index	Needs LLM
list_templates	List available extraction templates	—	—
info	Stats for a KA (template, counts, index status)	—	—
search	Semantic retrieval over a KA	✓	embedder
ask	RAG question-answering over a KA	✓	✓
export_obsidian	Export a KA to an Obsidian vault	—	embedder

All tools take a ka_path (a directory created by he parse). search/ask require an index — build it with he build-index.

export_obsidian requires the Obsidian export feature (see he export obsidian). If it is unavailable, the tool returns an explanatory message instead of failing.

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Example session

list_templates()                        → [{name: "general/biography_graph", ...}, ...]
info(ka_path="./tesla_kb")              → {nodes: 48, edges: 70, index_built: true, ...}
search(ka_path="./tesla_kb",
       query="War of Currents")          → {nodes: [...], edges: [...]}
ask(ka_path="./tesla_kb",
    question="Who were Tesla's rivals?")  → "Thomas Edison ..."
export_obsidian(ka_path="./tesla_kb",
                output="./vault")          → "Exported 49 notes to ./vault"