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

io.github.adelaidasofia由 GitHub 账号 adelaidasofia 发布v0.1.0更新于 Oct 24, 2026

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

发布方自己的服务:Fastio

已验证STDIO仅桌面效率与工作流知识与记忆

概览

AI 生成的概览

一个本地 MCP 服务器,读取预先计算好的 Obsidian 知识图谱,为笔记建议 wikilink、枢纽节点和所属社区。

可以做什么
通过词元重叠将笔记内容与图谱节点标签匹配,为笔记建议 wikilink,并对高度连接的节点(度数前 20)给予额外权重。判断笔记最接近属于哪些图谱社区。将接受、拒绝或忽略的建议记录到本地 SQLite 数据库,逐步积累反馈数据集。图谱在启动时加载一次并缓存在内存中。
适用场景
适合在 Obsidian 中写作、且已有 graphify 生成的 graph.json 的用户,希望助手在起草笔记时提出交叉链接和社区归属建议。没有预计算图谱时用处不大。
运行要求
以本地进程(stdio)在桌面端运行,可通过 MCP 包或 Python/FastMCP 方式安装。需要设置 GRAPH_JSON_PATH 指向 networkx node_link_data 格式的 graphify graph.json;AUTOTAGGER_DB 可选设置 SQLite 日志路径。
安装前请注意
README 说明该插件在每台机器首次加载时会向外部站点发送一次匿名安装信号,包含插件名称和版本;可用 MYCELIUM_NO_PING=1 退出。决策日志会写入本地 SQLite 文件。笔记内容仅在本地与图谱匹配。

安装

在 SourceWeft 中

  1. 打开 控制台中的 FastMCP server for Obsidian wikilink suggestions from a pre-computed knowledge g,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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.

来源:README.md,提交 5da031e

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.0最新Sep 16, 2026

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