How to Use the Markdown Frontmatter Harvester MCP in LlamaIndex

Q: How do I index my notes with Markdown Frontmatter Harvester and LlamaIndex?

You call the harvestmarkdownfrontmatter tool to scan your vault directory first. LlamaIndex then takes the resulting JSON array and loads it as document metadata for your vector index.

Q: Can LlamaIndex update its index when Markdown Frontmatter Harvester runs?

Yes, you can trigger the harvestmarkdownfrontmatter tool as a pre-processing step in your ingestion pipeline. This ensures your LlamaIndex vector store always reflects the latest tags and dates from your vault.

Index your Obsidian and Hugo vault metadata into LlamaIndex using this local MCP Server.

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Connect Markdown Frontmatter Harvester MCP to LlamaIndex

Create your Vinkius account to connect Markdown Frontmatter Harvester 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 Markdown Frontmatter Harvester with LlamaIndex

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Query vault metadata with LlamaIndex MCP Server

The `harvest_markdown_frontmatter` tool gathers all YAML tags, dates, and custom metadata fields from your local markdown files. It compiles this structured data into a single JSON object that your agent can query instantly. LlamaIndex takes this output and indexes the metadata fields directly. Precise semantic searches based on actual note attributes replace generic raw text matches.

Avoid context window bloat in RAG

The `harvest_markdown_frontmatter` tool extracts only the frontmatter block from your files, ignoring the main content body. This keeps the retrieved payload small and highly structured. By feeding only the metadata JSON to your LlamaIndex pipelines, you avoid wasting tokens on irrelevant text. Your agent filters notes by date or status before deciding which full files to read.

Build dynamic local knowledge graphs

The `harvest_markdown_frontmatter` tool maps the connections between your files using the tags and links found in your YAML blocks. It outputs these relationships in a clean, machine-readable format. LlamaIndex uses this structured relationship data to build more accurate indexes of your personal vault. Your agent navigates your notes using the actual structure you created in Obsidian or Hugo.

Setup guide

Set up Markdown Frontmatter Harvester 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 Markdown Frontmatter Harvester 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 Markdown Frontmatter Harvester tools.",
)
response = await agent.run("List recent Markdown Frontmatter Harvester data")

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Common questions about Markdown Frontmatter Harvester MCP in LlamaIndex

You call the `harvest_markdown_frontmatter` tool to scan your vault directory first. LlamaIndex then takes the resulting JSON array and loads it as document metadata for your vector index.

Yes, you can trigger the `harvest_markdown_frontmatter` tool as a pre-processing step in your ingestion pipeline. This ensures your LlamaIndex vector store always reflects the latest tags and dates from your vault.

The tool recursively traverses all subfolders starting from the absolute directory path you provide. It finds every markdown file and extracts its frontmatter, regardless of how deep the file sits in your folder structure.

The tool specifically targets files with the .md extension that contain standard YAML frontmatter blocks at the very top. It ignores binary files, images, and non-markdown assets in your vault.

Your local markdown YAML frontmatter stays entirely on your local file system during the scan. The server handles all parsing locally in a secure sandbox, meaning zero external network calls are made to process your files.

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