Markdown Frontmatter Harvester MCP. Query Metadata Across Your Entire Local Vault
Markdown Frontmatter Harvester indexes your local knowledge base by scanning Obsidian or Hugo vaults and extracting all YAML metadata into a single, queryable JSON file. It lets your AI agent instantly read tags, dates, statuses, and other notes' hidden data without needing to search thousands of scattered markdown files.
Give Claude and any AI agent real-world access
The MCP scans massive local directories to build a unified index of metadata from all contained markdown files.
It pulls out named data points like tags, dates, and status markers written in YAML frontmatter.
Your agent queries the generated JSON index directly, allowing precise filtering across thousands of documents at once.
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What AI agents can do with Markdown Frontmatter Harvester: 1 Tool
Use the available tool to scan your entire notes directory and create an instant, queryable index of all metadata found in your markdown files.
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Start using Markdown Frontmatter Harvester MCPHarvest Markdown Frontmatter
Provide the absolute directory path to scan local Markdown files and extract all YAML tags, dates, and metadata into an index.
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The Metadata Black Box Problem
Today, if your notes live in Obsidian or Hugo, the important details—like whether a note is marked 'draft' or belongs to the 'research' tag—are tucked away in YAML frontmatter blocks at the top of files. When you try to ask an AI client about these details, it often fails because it can't read thousands of local files quickly enough to build a complete picture.
With this MCP, your agent sees the full story. It scans the whole folder and rips out only that hidden metadata, packaging it into one clean JSON index. Your AI gets a unified database view, letting you ask sophisticated questions about your entire archive without any guesswork.
Markdown Frontmatter Harvester gives you structured data access
Manually auditing notes means opening folders, searching file names, and copy-pasting tags or dates into a spreadsheet just to count items. It's slow, prone to error, and terrible for large vaults.
Now you give the MCP the path, and it handles all the indexing work instantly. You get back a single JSON index that makes querying your entire knowledge base fast, reliable, and simple.
What Markdown Frontmatter Harvester MCP does for your AI
Writing with digital notes means using tools like Obsidian or Hugo, which rely on YAML 'frontmatter'—those little blocks at the top of a file that hold metadata like status: draft or tags: [idea]. When your AI client asks, 'Which posts are marked as drafts from 2024?', it usually fails because it can't quickly index every single local markdown file.
This MCP fixes that. It acts like a hyper-fast librarian, recursively scanning your entire folder structure and stripping out only the YAML frontmatter from every document. The result is a clean JSON index of your whole vault. Your agent gets one structured data set it can filter, sort, and query instantly, giving you reliable answers about your scattered notes.
019e38bc-5d28-737c-95e1-1dd334257389 How to set up Markdown Frontmatter Harvester MCP
The bottom line is you get one clean, actionable index of your entire knowledge base instead of thousands of individual files.
You provide the MCP with the absolute path to your entire notes folder or vault.
The tool scans every markdown file in that directory and extracts all the YAML frontmatter data it finds, ignoring the body text.
It returns a single, unified JSON object containing metadata for every file found, which your AI client can then use for querying.
Who uses Markdown Frontmatter Harvester MCP
This MCP is essential for researchers, technical writers, and content managers who rely on structured notes. If you're tired of asking your AI client questions that fail because it can only read the visible text in a single file, this tool gives you the metadata context you need.
Uses it to quickly find all articles marked with a specific status or tag across multiple drafts before publishing.
Leverages it to count how many notes touch upon a certain year or topic, allowing them to track research progress over time.
Uses it to audit an entire blog repository and identify every piece of content that hasn't been updated since a specific date.
Benefits of connecting Markdown Frontmatter Harvester MCP
Instant Querying: Instead of manually searching file names or using complex local scripts, your agent queries a unified JSON index. You get immediate answers about metadata like tags and status.
Massive Scale: It scans 1,000+ files in milliseconds, making it practical for large Obsidian vaults without slowing down your AI client's response time.
Data Structure: The output is clean YAML frontmatter converted into structured JSON. This format is ideal for any agent to consume and reason over.
Air-Gapped Security: Your private journal entries and business notes never leave your machine; the processing happens locally, maintaining 100% privacy.
Zero Setup: You don't need complex coding or configuration files. Just point the MCP at your root folder, and it does the rest.
Markdown Frontmatter Harvester MCP use cases
Finding all outdated drafts
A content manager needs to know which blog posts were created before 2023 but still have a 'status: draft' tag. They simply ask their agent, and the MCP uses harvest_markdown_frontmatter to generate an index, allowing the AI client to list every file that meets both criteria.
Auditing research topics
A researcher wants to count how many notes they wrote in 2024 about 'quantum computing' based on the date and tag fields. The agent runs harvest_markdown_frontmatter against their vault path, generating a clean dataset that lets the AI client perform an accurate count.
Listing urgent items
A student asks to see every note marked with the 'urgent' tag across three different subfolders. The agent runs harvest_markdown_frontmatter on the parent directory, providing a single index that lets it pull all relevant file names instantly.
Markdown Frontmatter Harvester MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Passing raw folders to AI
Asking your agent to 'read my notes folder' and expecting it to magically know the metadata structure, or relying on a simple search function that only reads visible text.
You must use the harvest_markdown_frontmatter tool. By providing the absolute directory path, you force the MCP to index the YAML data first, giving your agent structured access to tags and dates.
When to use Markdown Frontmatter Harvester MCP
Use this MCP if your problem is about metadata—if you need to filter based on things like status: draft, or count notes by a specific date field. This isn't for simple text searching; it's for structured querying of hidden data.
Don't use this if all you want is to search the actual words inside your documents, like finding every instance of 'project failure'. For that, you just need standard file reading capabilities. You only need this MCP when you have a knowledge base full of files and you need to query their properties, not their content.
Frequently asked questions about Markdown Frontmatter Harvester MCP
How does Markdown Frontmatter Harvester read my local Obsidian vault? +
The MCP scans the absolute directory path you provide. It specifically targets YAML frontmatter blocks within markdown files to extract tags, dates, and status markers.
Is this tool private or does it upload my notes? +
No, it's entirely air-gapped. Your journal entries and business notes never leave your machine; the processing happens locally on your system for maximum privacy.
What file types can harvest_markdown_frontmatter handle? +
It is designed to scan Markdown files (like those used in Obsidian or Hugo) and extract the YAML frontmatter contained within them.
Does this MCP read the body text of my notes? +
No, it only reads the metadata. It extracts the structured YAML data at the top of the file; the actual content of your note is ignored during indexing.