graph-autotagger-mcp

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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

Tool	What it does
suggest_links	Suggest wikilinks for a note based on token overlap with graph node labels
check_god_nodes	Check which highly-connected nodes (top 20 by degree) are relevant to the note
community_match	Find which graph communities the note most closely belongs to
log_suggestion_decision	Log 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]"

<details> <summary>Legacy install</summary>

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]"

</details>

Environment variables

Variable	Default	Description
GRAPH_JSON_PATH	~/vault/.graph/graph.json	Path to your graphify output
AUTOTAGGER_DB	~/.config/graph-autotagger/log.db	SQLite 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

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