Give Your AI Full Access to Your Obsidian Vault
Your Obsidian vault is your second brain. Years of notes, project plans, daily journals, meeting records, research — all connected with wikilinks and tags in a carefully organized folder of Markdown files.
But your AI can't see any of it. You copy-paste snippets into ChatGPT. You describe your note structure to Claude. You manually relay information between your knowledge base and your AI.
That ends now. The Obsidian bundle is a standard MCP server — the tools show up natively in whatever AI you already use. Claude Desktop, ChatGPT, Cursor, Windsurf, the mcpbundles CLI, or any MCP-compatible client. No special integration. Your AI sees the Obsidian tools the same way it sees any other tool in its context.
35 tools. Your AI becomes an Obsidian power user.
The Obsidian MCP bundle gives your AI the same access to your vault that you have — and then some. It reads notes and gets back structured data — parsed frontmatter, tags, file stats — not just raw text. It browses your folder hierarchy. It searches across every note in your vault with relevance scoring, regex patterns, frontmatter queries, and date ranges.
It writes. Not just "dump a whole file" writing. Your AI can create notes with full frontmatter, append entries to existing notes, and — this is the part that matters — surgically edit specific sections of a document without touching anything else.
It sees your images. It analyzes your graph structure. It finds orphaned notes and broken links. It lists every task across your entire vault. More on all of this below.
Surgical edits change everything
Most integrations that touch files do the same thing: read the whole file, modify it in memory, overwrite the whole file. Fine for code. Terrible for a living document with dozens of sections, tasks, and metadata fields that you don't want an AI to accidentally mangle.
The Obsidian PATCH operation works differently. Your AI targets a specific heading, block reference, or frontmatter field and inserts, replaces, or appends content just there.
Say you've got a project plan with milestones, discussion notes, and action items. You tell your AI to add two items to the milestones section. It reads the document map (a lightweight call that returns all headings, block refs, and frontmatter fields), finds the right heading, and appends only to that section. Your discussion notes and action items stay exactly as they were.
The same precision works for frontmatter. Your AI can flip status: draft to status: shipped on a single field without rewriting the YAML block. It can add a new reviewer: Tony field that didn't exist before. It can target nested heading paths like "Launch Plan::Key Milestones" to reach the right section in a deeply structured document.
This is the difference between an AI that can edit text files and an AI that understands Obsidian's document structure.
Daily notes are the fast path
The most common Obsidian workflow is appending to today's daily note. A quick thought, a task, a meeting summary — you open today's note and add a line.
Your AI does the same thing in one call. No need to figure out today's date, construct the filename, check if the file exists. Just "append this to my daily note." It handles the rest, including creating the note if it doesn't exist yet.
This turns your AI into a persistent journal. Every conversation can leave a trace in your vault. Meeting summaries go into the daily note. Research findings get filed in project notes. Action items land where they belong. Your AI doesn't just answer questions — it maintains your knowledge base while it works.
It controls Obsidian itself
This goes beyond file operations. Your AI can run any Obsidian command — the same commands you'd trigger from the command palette. Open the graph view. Insert a template. Export to PDF. Toggle a checklist item. If there's a command ID for it, your AI can execute it.
It can also open specific notes in the Obsidian UI, bringing them into focus so you can review what was just created or modified. This means your AI can write a note and then show it to you, rather than making you go find it.
Your AI can see your images
This is the one that changes the game for multimodal workflows. When your AI reads an image from your vault, it doesn't get a file path or a base64 blob dumped into a text response. It gets the actual image as an MCP ImageContent block — the same way a screenshot tool returns visual content.
That means any AI model with vision — Claude, GPT-4o, Gemini — actually sees the image. Your AI can describe a diagram, read handwritten notes from a photo, interpret a screenshot, or analyze a chart. All from files already sitting in your vault.
The practical use cases stack up fast. You have architecture diagrams in your vault — your AI reads the image and explains the data flow. You photographed a whiteboard after a brainstorming session — your AI transcribes the sticky notes into structured tasks. You have UI mockups saved as PNGs — your AI compares them against your spec document in the same vault.
PNG, JPEG, GIF, WebP, BMP, and SVG are all supported. The image flows through the same proxy tunnel as everything else — nothing gets stored on MCPBundles servers.
Graph analysis and vault maintenance
Obsidian's power comes from connections between notes. Wikilinks turn a folder of Markdown files into a knowledge graph. But maintaining that graph — finding orphans, fixing broken links, understanding relationships — is manual work. Until now.
Your AI traverses your link graph. Graph neighbors does a breadth-first search from any note, finding every connected note within a configurable depth. Direction matters: outgoing links, incoming backlinks, or both. Depth 1 shows direct connections. Depth 2 reveals the notes connected to those connections. This is Obsidian's graph view, but as structured data your AI can reason about.
Orphan detection scans every note in your vault and identifies the ones with zero incoming wikilinks — notes that nothing else links to. These are the ones you forgot about, the stubs you never connected, the ideas that fell through the cracks. Your AI finds them and can help you decide whether to connect, consolidate, or archive them.
Broken link detection does the inverse: it scans every wikilink in every note and checks whether the target actually exists. That reference to [[Old Project Name]] you renamed three months ago? Found.
Task management across your entire vault
Obsidian is great for tasks — the checkbox syntax (- [ ] do the thing) works in any note. But there's no built-in way to see tasks across your entire vault. Your AI can.
The task listing tool scans every note, extracts every checkbox, and returns them with their source file and line number. Filter by status (open, completed, all), by folder, by tag, or by keyword. Ask your AI "what are my open tasks tagged with Q2?" and get an answer without installing any plugins.
This is different from a Dataview query. It works without Dataview installed, it's available to your AI without you needing to write DQL syntax, and the results come back as structured data the AI can act on — not just display.
Templates at your fingertips
Your AI can list every template in your vault's template folder, preview their content, and use them as the basis for new notes. Ask it to create a new project note using your standard template and it reads the template, fills in the placeholders, and writes the result to your vault.
How the proxy tunnel works
Obsidian runs on your desktop. AI services run in the cloud. The MCPBundles desktop proxy bridges them with an encrypted tunnel.
AI → MCPBundles → Proxy Tunnel → Your Desktop → Obsidian (localhost:27124)
Your vault data flows through the tunnel in real time. Nothing gets stored on MCPBundles servers. The proxy handles Obsidian's self-signed TLS certificate automatically, so there's no certificate configuration to deal with. Start the proxy when you want access, stop it when you're done.
Works with the AI you already use
MCPBundles is a remote MCP server. You connect it once and the Obsidian tools appear in your AI's tool list — alongside any other bundles you've enabled. No local server to run, no JSON config files to maintain, no per-client setup.
Claude Desktop, ChatGPT, Cursor, Windsurf — add the MCPBundles MCP server URL and the tools are there. Your AI calls them the same way it calls any other tool.
mcpbundles CLI — for terminal workflows, scripting, and automation. Same tools, same proxy tunnel, same credentials.
Any MCP-compatible client — if it speaks MCP, it works. The tools are standard MCP tools with proper schemas, annotations, and content types. The image tool returns ImageContent that vision-capable models receive as an actual image in their context.
Five minutes to set up
1. Install the Obsidian plugin
Install the Local REST API community plugin in Obsidian. It's by Adam Coddington — search for it in Community Plugins, install, enable, and copy the API key from the plugin settings.
2. Start the proxy
Obsidian runs locally, so you need the desktop proxy to bridge the connection:
pip install mcpbundles
mcpbundles login
mcpbundles proxy start
3. Enable the bundle
Go to MCPBundles, enable the Obsidian bundle, and paste your API key. The 35 tools are now available to every AI client connected to your MCPBundles server.
What this actually looks like in practice
You're prepping for a team meeting. Instead of opening Obsidian, creating a file, typing out a template, you tell your AI: "Create meeting notes for the Q2 planning sync with attendees Tony and Sarah, agenda: hiring, roadmap, budget." A fully structured note appears in your vault with frontmatter, sections, and wikilinks to related project notes.
After the meeting, you tell your AI to append the action items. It doesn't overwrite your agenda and discussion notes — it surgically appends to the action items section.
Later that evening, you ask your AI to search your vault for everything related to "database migration." It finds four notes across different projects, reads them, and creates a consolidated summary note linking back to the originals. Then it appends a line to your daily note: "Created migration summary — see Projects/db-migration-summary.md."
You photographed a whiteboard after a brainstorming session and dropped the image into your vault. You tell your AI to read it. It sees the photo — the actual image, not a file reference — reads the sticky notes, and creates structured tasks in a new project note with wikilinks back to the brainstorming session.
Your vault has grown to 500 notes. You ask your AI to run a health check. It finds 23 orphaned notes that nothing links to, 7 broken wikilinks pointing to renamed or deleted notes, and 45 open tasks scattered across 12 different project files. It creates a vault maintenance summary with links to every issue, sorted by priority.
You're working on a system architecture and have a diagram saved as a PNG in your vault. You ask your AI to read the diagram and compare it against your architecture notes. It sees the image, identifies the components, and points out that the notes describe a caching layer that isn't in the diagram.
None of this requires you to leave your AI chat. Whether you're in Claude Desktop, ChatGPT, Cursor, or a terminal — your vault stays organized because your AI knows how to work with it the same way you do.
Get started
pip install mcpbundles
mcpbundles login
mcpbundles proxy start
Enable the Obsidian bundle, add your API key, and start talking to your vault from whichever AI you use.