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sunub

Obsidian MCP Server

Create Document with Properties

create_document_with_properties

Analyze a markdown source file and write AI-generated frontmatter properties in a two-step workflow: first returns analysis instructions, then writes properties based on AI input.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourcePathYesThe path to the source markdown file to read and analyze (e.g., "draft/my-article.md")
outputPathNoThe path where the processed file with properties will be saved. If not provided, the source file will be updated in place.
overwriteNoIf set to true, existing properties will be overwritten by the AI-generated content. Default: false.
aiGeneratedPropertiesNoAI-generated properties based on content analysis. If provided, these will be used instead of internal analysis.
quietNoIf true, the final write operation will return a minimal success message.
Behavior4/5

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

With annotations providing only 'openWorldHint', the description adds substantial behavioral context by detailing the two-step process, the return of a structured payload and content preview, and the write operation. It is transparent about the workflow but could mention error handling if called incorrectly.

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

Conciseness5/5

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

The description is concise with two well-structured paragraphs. The first paragraph states the purpose, the second explains the two steps. No extraneous information.

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

Completeness5/5

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

Given the tool's complexity (two-step workflow, 5 parameters, nested objects) and the comprehensive schema (100% coverage), the description provides a complete overview including the workflow. The lack of output schema is compensated by mentioning the return payload. Annotations provide additional context.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema; it only explains the workflow, not the parameters themselves. Hence a 3 is appropriate.

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 identifies the tool as a two-step workflow for AI-generated frontmatter properties, specifying the resource (documents) and action (create with properties). It distinguishes itself by naming the specific steps and referencing the sibling tool 'write_property'.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('when an AI agent should orchestrate analysis and write in a consistent workflow') and implicitly contrasts it with alternatives by noting it uses the same write logic as 'write_property'.

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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