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FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge g

adelaidasofia/graph-autotagger-mcp
authSTDIOregistry active
Summary

Built for Obsidian users who want algorithmic wikilink suggestions from an existing knowledge graph. You run graphify separately to generate graph.json from your vault, then this server loads it into memory and exposes four tools: suggest_links does token overlap scoring between note content and graph node labels, check_god_nodes filters the top 20 highest-degree nodes for relevance, community_match tells you which graph clusters your note belongs to, and log_suggestion_decision writes accept/reject feedback to SQLite so you can tune thresholds over time. The graph stays read-only in the server; all mutations happen in your editor. Pairs naturally with vault-sync-mcp and graph-query-mcp from the same author if you're building a full MCP-driven note pipeline.

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graph-autotagger-mcp

License GitHub stars Last commit Open issues PyPI version PyPI downloads Built by Mycelium AI

A FastMCP server that reads a pre-computed Obsidian knowledge graph and suggests wikilinks for your notes. Designed as a companion to graphify — run graphify to build the graph, then this MCP surfaces connections as you write.

Tools

ToolWhat it does
suggest_linksSuggest wikilinks for a note based on token overlap with graph node labels
check_god_nodesCheck which highly-connected nodes (top 20 by degree) are relevant to the note
community_matchFind which graph communities the note most closely belongs to
log_suggestion_decisionLog accepted/rejected/ignored decisions to a local SQLite database

Install

Open Claude Code, paste:

/plugin marketplace add adelaidasofia/graph-autotagger-mcp
/plugin install graph-autotagger-mcp@graph-autotagger-mcp

After install, set GRAPH_JSON_PATH (see Environment variables below) and restart Claude Code, then ask:

"Suggest wikilinks for this note: [paste note content]"

Legacy install
pip install fastmcp
  1. Clone:

    git clone https://github.com/adelaidasofia/graph-autotagger-mcp.git
    cd graph-autotagger-mcp
    
  2. Set the path to your graph.json:

    export GRAPH_JSON_PATH="~/vault/.graph/graph.json"
    

    Or pass it at registration time (see below).

  3. Self-test:

    python3 server.py
    
  4. Register with Claude Code:

    claude mcp add graph-autotagger -s user -- \
      env GRAPH_JSON_PATH="$HOME/vault/.graph/graph.json" \
      python3 /path/to/graph-autotagger-mcp/server.py
    
  5. Restart Claude Code, then ask:

    "Suggest wikilinks for this note: [paste note content]"

Environment variables

VariableDefaultDescription
GRAPH_JSON_PATH~/vault/.graph/graph.jsonPath to your graphify output
AUTOTAGGER_DB~/.config/graph-autotagger/log.dbSQLite log for suggestion decisions

How it works

The server loads graph.json once at startup and caches it in memory. Node labels are tokenized and matched against note content using token overlap scoring. God Nodes (top 20 by degree) get special treatment since connecting to them creates high-value cross-links.

The log_suggestion_decision tool lets you build a feedback dataset over time: accepted suggestions can be used to tune the relevance threshold; rejected ones reveal graph noise.

graph.json format

Expects the output format from graphify (networkx node_link_data):

{
  "nodes": [{"id": "node-id", "label": "Node Label", "community": 0}],
  "links": [{"source": "node-a", "target": "node-b"}]
}

Note: networkx serializes edges under "links", not "edges".

Related MCPs

Same author, same architecture pattern (FastMCP, draft+confirm on writes where applicable, vault auto-export, MIT):

  • slack-mcp - multi-workspace Slack
  • imessage-mcp - macOS iMessage
  • whatsapp-mcp - WhatsApp via whatsmeow
  • google-workspace-mcp - Gmail / Calendar / Drive / Docs / Sheets
  • apollo-mcp - Apollo.io CRM + sequences
  • substack-mcp - Substack writing + analytics
  • luma-mcp - lu.ma events
  • parse-mcp - markitdown / Docling / LlamaParse router
  • rescuetime-mcp - RescueTime productivity data
  • graph-query-mcp - vault knowledge graph queries
  • investor-relations-mcp - seed-raise pipeline tracker
  • vault-sync-mcp - bidirectional vault sync

Telemetry

This plugin sends a single anonymous install signal to myceliumai.co the first time it loads in a Claude Code session on a given machine.

What is sent:

  • Plugin name (e.g. slack-mcp)
  • Plugin version (e.g. 0.1.0)

What is NOT sent:

  • No user identifiers, names, emails, tokens, or API keys
  • No file paths, message content, or anything from your work
  • No IP address is stored after dedup processing

Why: Helps the maintainer know which plugins people actually install, so attention goes to the ones that get used.

Opt out: Set the environment variable MYCELIUM_NO_PING=1 before launching Claude Code. The hook will skip the network call entirely. Already-pinged installs leave a sentinel at ~/.mycelium/onboarded-<plugin> — delete it if you want to reset state.

License

MIT


Built by Mycelium AI. Full install or team version at diazroa.com.

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Configuration

GRAPH_JSON_PATH*

Absolute path to your graphify-out/graph.json

Categories
Documents & Knowledge
Registryactive
Packagehttps://github.com/adelaidasofia/graph-autotagger-mcp/releases/download/v0.1.0/graph-autotagger-mcp.mcpb
TransportSTDIO
AuthRequired
UpdatedMay 20, 2026
View on GitHub

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