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

io.github.adelaidasofia由 GitHub 帳號 adelaidasofia 發布v0.1.0更新於 Oct 24, 2026

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

發布方自己的服務:Fastio

已驗證STDIO僅桌面效率與工作流知識與記憶

概覽

AI 產生的概覽

一個本機 MCP 伺服器,讀取預先計算好的 Obsidian 知識圖譜,為筆記建議 wikilink、樞紐節點與所屬社群。

可以做什麼
透過詞元重疊將筆記內容與圖譜節點標籤比對,為筆記建議 wikilink,並對高度連結的節點(度數前 20)給予額外權重。判斷筆記最接近屬於哪些圖譜社群。將接受、拒絕或忽略的建議記錄到本機 SQLite 資料庫,逐步累積回饋資料集。圖譜在啟動時載入一次並快取於記憶體中。
適用情境
適合在 Obsidian 中寫作、且已有 graphify 產生的 graph.json 的使用者,希望助理在起草筆記時提出交叉連結與社群歸屬建議。沒有預先計算的圖譜時用處不大。
執行需求
以本機程序(stdio)在桌面端執行,可透過 MCP 套件或 Python/FastMCP 方式安裝。需要設定 GRAPH_JSON_PATH 指向 networkx node_link_data 格式的 graphify graph.json;AUTOTAGGER_DB 可選設定 SQLite 日誌路徑。
安裝前請注意
README 說明此外掛在每台機器首次載入時會向外部網站送出一次匿名安裝訊號,包含外掛名稱與版本;可用 MYCELIUM_NO_PING=1 退出。決策日誌會寫入本機 SQLite 檔案。筆記內容僅在本機與圖譜比對。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge g,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

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
bash
pip install fastmcp
  1. Clone:

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

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

    Or pass it at registration time (see below).

  3. Self-test:

    bash
    python3 server.py
  4. Register with Claude Code:

    bash
    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):

json
{  "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-<plugin> — delete it if you want to reset state.

License

MIT


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

來源:README.md,提交 5da031e

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.1.0最新Sep 16, 2026

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