FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge g
LocalAbout FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge g
A FastMCP server for Obsidian users that suggests note links from a precomputed graphify knowledge graph. It also identifies relevant highly connected nodes and communities, and records suggestion decisions locally.
Key features
- 01Suggest wikilinks from graph labels
- 02Find relevant highly connected nodes
- 03Match notes to graph communities
- 04Log decisions in SQLite
Use cases
- 01Find graph connections while writing an Obsidian note
- 02Identify central nodes related to a note
- 03Track accepted and rejected link suggestions
MCP support
Exposes graph-based link suggestions, community matching, and decision logging as tools for MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEgraph-autotagger-mcp
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]"
Legacy install
pip install fastmcp
-
Clone:
git clone https://github.com/adelaidasofia/graph-autotagger-mcp.git cd graph-autotagger-mcp -
Set the path to your graph.json:
export GRAPH_JSON_PATH="~/vault/.graph/graph.json"Or pass it at registration time (see below).
-
Self-test:
python3 server.py -
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 -
Restart Claude Code, then ask:
"Suggest wikilinks for this note: [paste note content]"
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
Built by Mycelium AI. Full install or team version at diazroa.com.
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