graph-autotagger-mcp
A FastMCP server that suggests wikilinks for Obsidian notes based on a pre-computed knowledge graph, helping surface connections while writing.
README
graph-autotagger-mcp
<!-- mycelium-badges:start -->
<p> <a href="https://github.com/adelaidasofia/graph-autotagger-mcp/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/adelaidasofia/graph-autotagger-mcp?color=blue"></a> <a href="https://github.com/adelaidasofia/graph-autotagger-mcp/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/adelaidasofia/graph-autotagger-mcp?color=eab308"></a> <a href="https://github.com/adelaidasofia/graph-autotagger-mcp/commits/main"><img alt="Last commit" src="https://img.shields.io/github/last-commit/adelaidasofia/graph-autotagger-mcp"></a> <a href="https://github.com/adelaidasofia/graph-autotagger-mcp/issues"><img alt="Open issues" src="https://img.shields.io/github/issues/adelaidasofia/graph-autotagger-mcp"></a> <a href="https://pypi.org/project/adelaidasofia-graph-autotagger-mcp/"><img alt="PyPI version" src="https://img.shields.io/pypi/v/adelaidasofia-graph-autotagger-mcp?color=blue&label=pypi"></a> <a href="https://pypi.org/project/adelaidasofia-graph-autotagger-mcp/"><img alt="PyPI downloads" src="https://img.shields.io/pypi/dm/adelaidasofia-graph-autotagger-mcp?color=blue&label=downloads"></a> <a href="https://myceliumai.co"><img alt="Built by Mycelium AI" src="https://img.shields.io/badge/built_by-Mycelium_AI-15B89A"></a> </p>
<!-- mycelium-badges:end -->
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
-
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]"
</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
Built by Mycelium AI. Full install or team version at diazroa.com.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。