Retrieves and searches the content of Markdown (.md, .mdx) documents stored in an Obsidian vault. Use this tool to find notes related to a specific topic or keyword and understand their core content.

When to use:

• When you need to find a specific note by its title or a keyword to check its content.

• When you want to synthesize and summarize information scattered across multiple notes.

• When looking for answers to questions based on your saved records, such as "What was the project deadline?"

• To discover connections by finding all notes that link to a specific note.

• When you need to retrieve a list of unfinished tasks (- [ ]) from daily notes or meeting minutes.

• Use "index_vault_to_vectordb" to manually trigger a full re-indexing of the vault for the vector database.

Returns the content of the most relevant document(s) in text format. It can also include metadata such as the document's title, tags, and creation date.

Requirements: The user's Obsidian Vault path must be correctly configured in an environment variable or a similar setting. For searches, use the exact filename or include core keywords for content-based queries.

IMPORTANT INSTRUCTION FOR AI: If the tool response includes a 'diagnostic_message' (usually found at the beginning of the text or in a tag), you MUST relay this information to the user exactly once. It contains essential advice on how to improve search quality by running a local setup script via 'npx @sunub/obsidian-mcp-server setup'. Do not ignore it.

Reads a target markdown document and returns an AI-facing payload for generating frontmatter properties.

This tool does not write to disk. It returns content_preview and a target output schema so an AI can produce a valid property object.

Use Cases:

• After completing a draft, when you need property suggestions from content.

• When missing frontmatter fields (title, tags, summary, slug, date, category, completed) should be generated.

To apply generated properties to a file, call 'write_property' with the resulting JSON.

Description: Adds or updates properties within the frontmatter section at the top of a specified Obsidian markdown file. This tool is primarily used to apply metadata generated by the 'generate_property' tool to an actual file.

Parameters:

• filePath (string, required): The path to the target markdown file to which properties will be added or updated. Example: "my-first-post.md"

• properties (object, required): A JSON object containing the key-value pairs to be written to the file's frontmatter. If a property with the same key already exists in the file, it will be overwritten with the new value.

Example:

JSON { "title": "Optimizing I/O Handling in a Serverless Environment", "date": "2025-04-03", "tags": ["serverless", "optimization"], "summary": "A case study on optimizing I/O in a serverless environment by benchmarking Promise.all and Workers.", "completed": true }

Return Value:

Upon successful execution, it returns a JSON object containing the status, a confirmation message, and the property object that was applied to the file.

Example:

JSON { "status": "success", "message": "Successfully updated properties for my-first-post.md", "properties": { "title": "Optimizing I/O Handling in a Serverless Environment", "date": "2025-04-03", "tags": ["serverless", "optimization"], "summary": "A case study on optimizing I/O in a serverless environment by benchmarking Promise.all and Workers.", "completed": true } }

Dependencies & Requirements:

• Input Data: The properties parameter should typically be the JSON object output from the 'generate_property' tool.

• Environment Setup: The absolute path to the user's Obsidian Vault must be correctly set as an environment variable.

Starts and completes a two-step workflow for AI-generated frontmatter properties.

Step 1: Call this tool with sourcePath (and optional outputPath). It returns a structured instruction payload and a content preview for AI analysis. Step 2: Call this same tool again with aiGeneratedProperties. The tool then writes those properties by executing the same write logic used by the 'write_property' tool.

Use this tool when an AI agent should orchestrate analysis and write in a consistent workflow.

Scans a specified markdown file for linked images (or other attachments), moves them to a dedicated folder named after the document's title, and updates the links within the markdown file automatically.

Use Cases:

• When a post is finalized and you want to clean up all associated images into a neat folder.

• To automatically organize attachments for better vault management.

Example Workflow:

1. Specify 'my-awesome-post.md' as the fileName.

2. The tool finds the 'title' property in the frontmatter (e.g., "My Awesome Post").

3. It finds all image links like ![[my-image.png]].

4. It creates a folder at '{vault}/images/My Awesome Post/'.

5. It moves 'my-image.png' into that new folder.

6. It updates the link in the markdown file to ![[images/My Awesome Post/my-image.png]].