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Obsidian MCP Server

Generate Obsidian Property

generate_property

Reads a markdown file and returns an AI-facing payload to generate missing frontmatter properties. After a draft, use this tool to get suggestions for title, tags, and more, then apply with write_property.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYesThe name or path of the file to analyze and add properties to (e.g., "my-first-post.md")
overwriteNoIf set to true, existing properties will be overwritten by the AI-generated content. Default: false.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are sparse (only openWorldHint=true), but the description compensates by explicitly stating 'This tool does not write to disk' and describing the return payload (content_preview and target output schema). This provides sufficient behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two paragraphs with bullet points for use cases. It is concise and front-loaded with the core function. The extra sentences about use cases and linking to write_property earn their place, though the overwrite default contradiction adds unnecessary confusion.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains the return structure adequately. It covers use cases and references to sibling tools. However, it fails to mention the potential side effect of overwrite when combined with write_property, and the default value inconsistency hurts completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100%, the description contains a contradiction: it states 'Default: false' for the 'overwrite' parameter, but the input schema shows 'default': true. This inconsistency could mislead the agent. The description does add the context of overwriting existing properties, but the error reduces reliability.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Reads a target markdown document and returns an AI-facing payload for generating frontmatter properties.' The verb 'reads' and resource 'markdown document' are specific, and the distinction from the sibling 'write_property' is clear because it explicitly says this tool does not write to disk.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: 'After completing a draft, when you need property suggestions from content' and 'When missing frontmatter fields should be generated.' It also advises to use 'write_property' to apply the generated properties. However, it does not compare against all siblings like 'create_document_with_properties', which might have overlapping functionality.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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