原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Obsidian MCP Server

作者:maxkuminov版本 1.1.0i★ –i在你的电脑上运行
注册名称io.github.maxkuminov/obsidian-mcp

安装

概览

作者提供的说明(英文原文)

Self-hosted agent memory as a markdown vault: semantic and keyword search, wikilink graph, OAuth

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: Streamable HTTP
  • 分类: 文档、表格与办公
  • 你需要填写:DATABASE_URL, VAULT_PATH, SECRET_KEY, MCP_HOSTNAME, OPENAI_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: DATABASE_URL, VAULT_PATH, SECRET_KEY, MCP_HOSTNAME, EMBEDDING_PROVIDER, OLLAMA_URL, OPENAI_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "obsidian": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DATABASE_URL",
        "-e",
        "VAULT_PATH",
        "-e",
        "SECRET_KEY",
        "-e",
        "MCP_HOSTNAME",
        "-e",
        "EMBEDDING_PROVIDER",
        "-e",
        "OLLAMA_URL",
        "-e",
        "OPENAI_API_KEY",
        "ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
      ],
      "env": {
        "DATABASE_URL": "your-value",
        "VAULT_PATH": "your-value",
        "SECRET_KEY": "your-value",
        "MCP_HOSTNAME": "your-value",
        "EMBEDDING_PROVIDER": "your-value",
        "OLLAMA_URL": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add obsidian --env DATABASE_URL=your-value --env VAULT_PATH=your-value --env SECRET_KEY=your-value --env MCP_HOSTNAME=your-value --env EMBEDDING_PROVIDER=your-value --env OLLAMA_URL=your-value --env OPENAI_API_KEY=your-value -- docker run -i --rm -e DATABASE_URL -e VAULT_PATH -e SECRET_KEY -e MCP_HOSTNAME -e EMBEDDING_PROVIDER -e OLLAMA_URL -e OPENAI_API_KEY ghcr.io/maxkuminov/obsidian-mcp:v1.1.0

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "obsidian": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DATABASE_URL",
        "-e",
        "VAULT_PATH",
        "-e",
        "SECRET_KEY",
        "-e",
        "MCP_HOSTNAME",
        "-e",
        "EMBEDDING_PROVIDER",
        "-e",
        "OLLAMA_URL",
        "-e",
        "OPENAI_API_KEY",
        "ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
      ],
      "env": {
        "DATABASE_URL": "your-value",
        "VAULT_PATH": "your-value",
        "SECRET_KEY": "your-value",
        "MCP_HOSTNAME": "your-value",
        "EMBEDDING_PROVIDER": "your-value",
        "OLLAMA_URL": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "obsidian": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DATABASE_URL",
        "-e",
        "VAULT_PATH",
        "-e",
        "SECRET_KEY",
        "-e",
        "MCP_HOSTNAME",
        "-e",
        "EMBEDDING_PROVIDER",
        "-e",
        "OLLAMA_URL",
        "-e",
        "OPENAI_API_KEY",
        "ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
      ],
      "env": {
        "DATABASE_URL": "your-value",
        "VAULT_PATH": "your-value",
        "SECRET_KEY": "your-value",
        "MCP_HOSTNAME": "your-value",
        "EMBEDDING_PROVIDER": "your-value",
        "OLLAMA_URL": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • DATABASE_URL 机密 必填 env
    PostgreSQL 16 + pgvector (0.8.0+) connection URL for the app's NON-superuser role, e.g. postgresql+asyncpg://obsidian_mcp: @postgres:5432/obsidian_mcp. A superuser role or a CHANGE_ME password is ref…
  • VAULT_PATH 必填 env
    Path of the Obsidian vault inside the container (mount your vault read-write there).
  • SECRET_KEY 机密 必填 env
    itsdangerous signer for cookies/sessions. Generate with `openssl rand -hex 32`; the app refuses to start on a placeholder.
  • MCP_HOSTNAME 必填 env
    Public hostname your reverse proxy routes to. Also derives BASE_URL, ALLOWED_ORIGINS and ALLOWED_HOSTS as https:// unless those are set explicitly.
  • EMBEDDING_PROVIDER env
    Embedding provider: `ollama` (self-hosted, default) or `openai` (bring your own key). Switching after deploy requires `make reset-embeddings`.
  • OLLAMA_URL env
    Ollama endpoint serving bge-m3 (used when EMBEDDING_PROVIDER=ollama). Must be https or loopback unless EMBEDDING_ALLOW_PLAINTEXT is set.
  • OPENAI_API_KEY 机密 env
    OpenAI (or OpenAI-compatible) API key for text-embedding-3-* (required when EMBEDDING_PROVIDER=openai; the server refuses to start without it).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 maxkuminov 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“文档、表格与办公”中的其他服务器比较i

  • 星标数多于此分类中其他 3528 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 3528 个服务器的 92% 更新
  • 基础检查结果等于或优于此分类中其他 3528 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Obsidian MCP Server 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:DATABASE_URL, VAULT_PATH, SECRET_KEY, MCP_HOSTNAME, OPENAI_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(1.1.0)于前天进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。

有什么可以替代它?

同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Agent-Native Calendar

作者:BuilderIO

Agent-Native Google Calendar - manage events, sync, and public booking

在云端运行★ 7,620MIT2个月前更新
安装

Excel MCP Server

作者:haris-musa

Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.

在你的电脑上运行★ 4,276MIT7天前更新
安装

MCP Server for Excel

作者:sbroenne

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

在你的电脑上运行★ 847MIT4天前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Obsidian MCP Server

Your agents' memory, in the vault you already have, reachable from claude.ai, ChatGPT, Claude Code and your phone.

A self-hosted Model Context Protocol server over an Obsidian vault. Three things it does that the alternatives mostly don't:

  • Your existing vault, served in place. No imposed note format, no sidecar syntax, no "memory" folder you have to adopt. Agents read what you read, follow your wikilinks, and pick up your folder layout, frontmatter schema and tag conventions from the vault itself.
  • Remote and multi-user. OAuth 2.0 PKCE and API keys, so claude.ai, Claude Desktop, Claude mobile, ChatGPT, Claude Code and n8n all read and write the same files, each user confined to their own vault root.
  • Writes cannot clobber. expected_hash preconditions refuse an edit made from a stale read, every publish is atomic and no-clobber by default, deletes go to .trash/, and dry_run returns the diff before anything lands.

Connect from Claude Code:

claude mcp add obsidian --transport http \

--url "https://obsidian-mcp. /mcp" \ --header "Authorization: Bearer omcp_..."

In claude.ai, Claude Desktop or ChatGPT, add a custom connector at https://obsidian-mcp. /mcp and sign in through the OAuth consent page. Connect a client has the rest.

Stack: Python 3.12, FastAPI, PostgreSQL with pgvector. Pluggable embeddings (Ollama — bge-m3 by default, qwen3-embedding:0.6b in the reference deployment — or OpenAI text-embedding-3-{small,large}).

Contents

  • Why this exists
  • A session at the keyboard
  • A session away from the keyboard
  • What's in the box
  • vs. other Obsidian MCP servers
  • vs. hosted memory systems
  • vs. an agent with raw file access
  • Who this is for
  • Control panel
  • Quick start
  • Versioning and compatibility
  • Upgrading
  • Cost expectations
  • The self-describing vault
  • Multi-user mode
  • Configuration
  • Architecture
  • Project layout
  • Development
  • Security notes

Why this exists

There are three things going on here, and they're more interesting together than apart.

1. Agent memory that you can actually read

If you let an agent run for a while, it needs memory. Most setups solve this with an opaque vector store, a SQLite blob, or a managed "memory" service that you can't see into. That works until you want to know what the agent thinks it knows about you, or you need to correct something, or you want to understand why it just made a weird suggestion.

This server gives you a different deal. Agent memory lives as markdown files in your vault. Folder structure, file names, frontmatter, all visible. You can open the file in Obsidian and read it. You can edit it. You can delete it. You can grep it. The agent's "memory" is a human-auditable artifact that sits in the same place as your own notes, with the same tools available.

The home lab is the use case that sold me on this. My vault has notes on the rack, the network, and every Home Assistant integration. I can say "set up a night-light mode in the master bathroom, 1% after 11pm" and a sysadmin agent finds the right config, makes the change, and updates the doc in the same pass. Six months later when I've forgotten how it works, the answer is in the vault, not buried in some chat history I can't search.

The semantic search and wikilink graph still work over that material, so retrieval is fast and conceptual. But the substrate is files you own, not a black box.

To be precise about the scope: what the server supplies is MCP-accessible storage, keyword and semantic search, and graph operations over markdown notes, for whatever MCP clients you connect. The agents direct their own reads and writes. There is no extraction or decay pipeline running behind them, and nothing rewrites a note because a model judged it stale: an agent remembers something because it wrote a note, and forgets it because someone deleted one. By late 2026 that is less of a contrarian position than it sounds. Every vendor's consolidation step (the "dreaming" or "sleep-time" job) has turned into a separate process you can inspect, rather than silent extraction inside the write path. This server keeps the agent as the author. The consolidation help it will grow is a hygiene report that proposes merges and flags duplicates, and never writes (#359), not a pipeline that edits your notes while you are not looking.

2. A shared memory layer between you and your agents

The other half runs the other way: the vault isn't only the agents' memory, it's mine. I think of my Obsidian vault as my exocortex. The "big me" that includes notes, calendars, scripts, search, and AI assistants is substantially more capable than the "small me" of the biological brain alone. It's also where I do most of my thinking, because writing something down is itself a form of thought.

The problem is that until recently, the vault was passive. I had to go find things. Agents that wanted to help me had to be briefed from scratch every session, and they had no way to see what I'd already written about a topic.

This server fixes that. Now the same vault feeds my own daily writing and any agent I plug into it. The agent reads what I read, links what I link, follows the same wikilinks, sees the same frontmatter. When I write a project note on Sunday, my Monday-morning briefing agent already knows about it. When the agent leaves notes from a research session, they show up in my normal Obsidian search.

A concrete version of this: I'll spend a session in Claude Code on a project, wrap up, push the commits, and then just say "update Obsidian." The agent reads the vault guide, figures out where project notes live in my structure, picks the right format and frontmatter, and leaves a session log I can later roll into a status report. No path-passing, no telling it what to write — the conventions are already in the vault, and it follows them.

That's the exocortex idea made concrete: one place that holds context, and both the human and the agents reading and writing into it on the same terms.

3. The vault follows you

The thing that still surprises me is that this is internet-facing. Same vault, same notes, reachable from anywhere I happen to be talking to an agent — Claude in a browser tab, Claude on my phone on the train, an n8n workflow firing on a schedule, a Claude Code session on whatever laptop is in front of me. All behind OAuth or a bearer key, all hitting the same source of truth.

So when I hear an interesting podcast on a commute and want to think out loud about it with Claude on my phone, the agent isn't starting cold. It can pull up what I've already written on adjacent topics, surface a note I half-finished six months ago, and at the end of the conversation suggest updates and write them in. The vault doesn't have to be near me to be the thing I'm working in. (A session away from the keyboard walks through exactly one of these.)

A session at the keyboard

To make the abstract concrete, a short transcript from a real session. Tool calls inline; the user types in plain English and the agent picks the right tool.

you  > find anything I've written about why retrieval-augmented

chat is hard

claude → semantic_search(query="why retrieval-augmented chat is hard") • 2025-12-03 - RAG failure modes.md • 2025-10-18 - Chunking tradeoffs.md • 2024-06-22 - Vector recall vs precision.md

the closest hit is your December note on RAG failure modes. let me see what it's connected to.

→ get_neighborhood(path="2025-12-03 - RAG failure modes.md", depth=1) 8 connected notes. notable ones: • "Hybrid search" — a re-ranking design you sketched but didn't ship • "Out-of-distribution queries" — a half-finished draft from October • "Embedding model choice" — links to bge-m3, your current model

you have three connected pieces here: a failure-modes catalog, an unshipped hybrid-search sketch, and an abandoned O-O-D draft. want me to summarize all three together, or pull up the draft so you can decide whether to finish it?

you > pull up the draft, and tag it #revisit-2026 in frontmatter while you're there

claude → read_note(path="2025-10-18 - OOD queries (draft).md") → set_frontmatter( path="2025-10-18 - OOD queries (draft).md", updates={"tags": ["draft", "rag", "revisit-2026"]} )

here's the draft, and the tag is on. you stopped right before the section on confidence thresholds; the open question you left yourself was…

Two things to notice. First, the agent didn't need to be told what folder to look in or what tools to use — it picked them. Second, the write at the end is structured (set_frontmatter mutating YAML, not a regex over the file body), so the note round-trips cleanly. The self-describing vault and the wikilink graph are doing the work that makes this feel natural.

A session away from the keyboard

The transcript above is the easy case: I'm at a desk, I can see what the agent is doing, and Obsidian is one alt-tab away. The session that actually changed how I think about this server had none of that.

I was out walking with a health podcast in my ears — a long one, two people who clearly disagreed with each other, an hour of it. I had my phone and no intention of going home to a laptop. So I pulled the episode's transcript, handed it to Claude on my phone, and we talked it through while I kept walking: what the actual claim was, which parts I already had notes on, where it cut against something I'd decided months ago and written down at the time.

The agent had the vault the whole way. It surfaced what I'd already written on the topic, flagged that two dates in an older note were wrong, and asked whether a decision I'd recorded last year still stood given what the episode argued. By the time I got back it had written all of it in: the health-related decisions I'd actually landed on during the walk, the date corrections in the old note, a couple of new notes on the episode itself — and, because the conversation kept circling back to it, a durable note on how I decide which experts to trust on medical questions in the first place. That last one is the artifact I keep returning to. It wasn't about the episode at all; it was the reasoning underneath a whole class of decisions, and it now sits in the vault where the next agent will find it.

I never opened Obsidian. Not on the walk, not when I got home. The whole session — retrieval, argument, correction, and the writing that came out of it — went through an agent, and the vault is simply where it landed. Obsidian is how I check the w

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