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 LlamaIndex 5 tools — connect in under 2 minutes

Name: Obsidian Publish
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Obsidian Publish as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Obsidian Publish. "
            "You have 5 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Obsidian Publish?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine Obsidian Publish tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 5 tools from Obsidian Publish

Why Use LlamaIndex with the Obsidian Publish MCP Server

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

Data-first architecture: LlamaIndex agents combine Obsidian Publish tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Obsidian Publish tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Obsidian Publish, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Obsidian Publish tools were called, what data was returned, and how it influenced the final answer

Obsidian Publish + LlamaIndex Use Cases

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

Hybrid search: combine Obsidian Publish real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Obsidian Publish to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Obsidian Publish for fresh data

Analytical workflows: chain Obsidian Publish queries with LlamaIndex's data connectors to build multi-source analytical reports

Obsidian Publish MCP Tools for LlamaIndex (5)

These 5 tools become available when you connect Obsidian Publish to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Obsidian Publish + LlamaIndex FAQ

Common questions about integrating Obsidian Publish MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Obsidian Publish tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.