How to Use the Obsidian Publish MCP in LlamaIndex

Q: Will LlamaIndex index my entire Obsidian Publish site at once?

You control the scope. Using listfiles, you can decide which paths to include or exclude, keeping your index focused and performant.

Q: Is the Obsidian Publish MCP server compatible with LlamaIndex filters?

Absolutely. You can use the metadata provided by getmetadata to create advanced retrieval filters in your LlamaIndex workflow.

Convert your notes into a searchable vector index with LlamaIndex using the Obsidian Publish MCP server.

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Connect Obsidian Publish MCP to LlamaIndex

Create your Vinkius account to connect Obsidian Publish to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Obsidian Publish with LlamaIndex

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Claude

Perplexity

Build a grounded RAG knowledge base

Stop relying on static training data for your notes. Use `get_file` to pull raw markdown and inject it directly into your vector store for semantic retrieval. LlamaIndex indexes these results automatically. This means your agent answers questions based on your actual site content rather than hallucinated facts.

Index structural hierarchies automatically

Use `list_navigation` to understand how your notes relate to each other. By mapping the tree structure, you provide the indexer with vital context about note importance. This creates a graph-aware index. Your queries gain precision because the agent understands the hierarchical relationship between different sections of your vault.

Refine knowledge with metadata filtering

Call `get_metadata` to grab creation hashes and frontmatter attributes before indexing. This allows you to filter your search space by tags or dates. LlamaIndex uses these attributes to partition your vector store. You get faster results and higher relevance when querying your Obsidian Publish site.

Setup guide

Set up Obsidian Publish MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Obsidian Publish MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Obsidian Publish tools.",
)
response = await agent.run("List recent Obsidian Publish data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Obsidian Publish. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Obsidian Publish MCP in LlamaIndex

It calls the server to retrieve file contents and metadata. These are then converted into vector embeddings for accurate semantic searching.

You control the scope. Using `list_files`, you can decide which paths to include or exclude, keeping your index focused and performant.

Absolutely. You can use the metadata provided by `get_metadata` to create advanced retrieval filters in your LlamaIndex workflow.

Your agent can re-run the tool calls to fetch the updated content. LlamaIndex then updates the vector store to reflect the latest version of your notes.

The server uses a strict, token-based security model. Only the notes explicitly requested by your agent are retrieved and indexed into your local vector store.

Use it with your favorite AI tools

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