How to Use the Markdown Frontmatter Harvester MCP in Google ADK

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Connect Markdown Frontmatter Harvester MCP to Google ADK

Create your Vinkius account to connect Markdown Frontmatter Harvester to Google ADK 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 Google ADK

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Structured vault metadata for Google ADK

Use `harvest_markdown_frontmatter` to feed your Gemini-powered agents a structured view of your local Obsidian vault. It turns scattered markdown files into a unified JSON format that plays well with Google Cloud infrastructure. This tool simplifies the process of providing context to agents that need to cross-reference documentation. It bridges the gap between your local files and your cloud-based agent logic.

Efficient context injection in Google ADK

The `harvest_markdown_frontmatter` tool generates a summary of your vault's frontmatter, which you can then pass to your agent. This allows your agent to reason over your entire note collection using its long-context capabilities. It removes the need for manual file uploads. Your agent can query your local vault structure as if it were a native database.

Scalable local vault parsing for cloud agents

By integrating `harvest_markdown_frontmatter` with your Google ADK toolset, you gain a clear view of your project status and tags. It runs locally and exposes only the necessary metadata to your agent. This workflow ensures your agents have the most current information about your notes. It makes it easy to build agents that track project progress based on your markdown frontmatter.

Setup guide

Set up Markdown Frontmatter Harvester MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Markdown Frontmatter Harvester tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Markdown Frontmatter Harvester_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Markdown Frontmatter Harvester tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Markdown Frontmatter Harvester MCP in Google ADK

Yes, you add it to your McpToolset and pass it to your agent. It integrates into your existing Google Cloud workflow without requiring custom boilerplate.

It provides a clear index of your notes. Your agent spends less time searching for information and more time reasoning over the metadata you provide.

The server runs locally, so your notes stay on your machine. Only the extracted metadata is sent to the agent for processing.

It traverses your entire directory structure automatically. You get a flat JSON representation of your nested Obsidian or Hugo vault.

It only reads YAML frontmatter from your markdown files. It does not touch your actual note content or private system configuration files.

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