Can the AI accurately recreate the folder structure of my vault?

Yes. When the model invokes `list_navigation`, it downloads the systematic JSON map of the entire public vault, thereby allowing it to understand the relationships and physical structure of your folders perfectly.

Does `get_file` automatically render complex Markdown components?

No rendering is performed on our end. The `get_file` tool returns pure raw Markdown (including frontmatter and obsidian syntax). Fortunately, LLMs (like Claude) are exceptionally adept at reading and reasoning over raw Markdown text structures.

If my Published site is private and requires a password, does it still work?

Yes, it works gracefully. If you configured Obsidian Publish with restricted read access, you can manually inject a persistent token into the setup, giving your AI agent the clearance to read securely behind the authentication wall.

Obsidian Publish MCP Server for LangChain 5 tools — connect in under 2 minutes

Name: Obsidian Publish
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Obsidian Publish through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "obsidian-publish": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Obsidian Publish, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Obsidian Publish MCP Server

Connect your Obsidian Publish environment to your AI agent and construct an intelligent oracle that reads smoothly from your personal or corporate markdown knowledge base.

LangChain's ecosystem of 500+ components combines seamlessly with Obsidian Publish through native MCP adapters. Connect 5 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Vault Crawling — Programmatically fetch your entire published vault structure utilizing list_files and list_navigation to build contextual trees.
Direct Note Access — Execute get_file to stream the complete raw markdown contents of any note directly into your chat workflow for fast summarization.
Metadata Operations — Use get_metadata to retrieve frontmatter properties, tags, and internal link logic mapped by Obsidian.
Site Auditing — Easily ping site_info to ensure connectivity and verify the deployment status of your target Obsidian publish endpoint.

The Obsidian Publish MCP Server exposes 5 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Obsidian Publish to LangChain via MCP

Follow these steps to integrate the Obsidian Publish MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 5 tools from Obsidian Publish via MCP

Why Use LangChain with the Obsidian Publish MCP Server

LangChain provides unique advantages when paired with Obsidian Publish through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Obsidian Publish MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Obsidian Publish queries for multi-turn workflows

Obsidian Publish + LangChain Use Cases

Practical scenarios where LangChain combined with the Obsidian Publish MCP Server delivers measurable value.

RAG with live data: combine Obsidian Publish tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Obsidian Publish, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Obsidian Publish tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Obsidian Publish tool call, measure latency, and optimize your agent's performance

Obsidian Publish MCP Tools for LangChain (5)

These 5 tools become available when you connect Obsidian Publish to LangChain via MCP:

get_file

Retrieve exact textual file content and binary assets

get_metadata

Extract internal creation hashes mapping a specific Markdown page

list_files

List all explicitly published raw file paths across the Obsidian workspace

list_navigation

Visualize structurally formatted Markdown navigation trees

site_info

Identify global configuration and styling mapping the site

Example Prompts for Obsidian Publish in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Obsidian Publish immediately.

"Check the vault and list all the files currently publicly available."

"Read the contents of 'System Requirements 2026.md'."

"Fetch the metadata and tags applied to my 'Inbox' note."

Troubleshooting Obsidian Publish MCP Server with LangChain

Common issues when connecting Obsidian Publish to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Obsidian Publish + LangChain FAQ

Common questions about integrating Obsidian Publish MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.