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

by adelaidasofiaversion 0.1.0i★ –iRuns on your computer
Registry nameio.github.adelaidasofia/graph-autotagger-mcp

Install

Overview

Description from the maker (original English)

FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge graph (graphify…

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Docs, sheets & office work
  • You need to fill in: GRAPH_JSON_PATH

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: GRAPH_JSON_PATH

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

Fill in the empty values before running it.

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • GRAPH_JSON_PATH required env
    Absolute path to your graphify-out/graph.json

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account adelaidasofia, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Docs, sheets & office worki

  • More stars than 0% of the other 3572 servers in this category
  • Published to the registry more recently than 10% of the other 3572 servers in this category
  • Basic checks equal to or better than 100% of the other 3572 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

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

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: GRAPH_JSON_PATH

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

graph-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

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

  1. Set the path to your graph.json:
   export GRAPH_JSON_PATH="~/vault/.graph/graph.json"

Or pass it at registration time (see below).

  1. Self-test:
   python3 server.py
  1. 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

  1. 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):

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- — 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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