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Obsidian MCP Learning System

Obsidian MCP Learning System

by dhruvvenkat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_study_session

Create a focused study session from an Obsidian vault folder by identifying recurring concepts and likely knowledge gaps, tailored to a target duration.

Instructions

Generate a focused study session using recurring concepts and likely gaps.

Args: vault_path: Absolute path to the Obsidian vault. folder: Folder inside the vault to scan. duration_minutes: Target study session length.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "generate_study_session"
  }
}

Input Schema

NameRequiredDescriptionDefault
folderNo
vault_pathNo/home/dhruv/Documents/compiler engineering
duration_minutesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full disclosure burden, and it omits the most important behavioral question: whether generating a session writes notes into the vault or is a read-only computation. It also says nothing about permissions, side effects, or what 'focused' or 'likely gaps' concretely mean in practice.

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 purpose sentence is front-loaded and the Args block is short with no filler. The Args list mostly restates parameter names with a brief gloss, so it is efficient if slightly redundant in form.

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?

An output schema exists, so return values need not be explained, but the description still leaves key gaps for a generation tool: whether it persists anything to the vault, how folder=null scoping behaves, and what distinguishes the session it produces. Adequate to attempt a call, not adequate to predict its effects.

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 0%, so the description must compensate, and it does partially: it clarifies vault_path is absolute and points at an Obsidian vault, folder is inside the vault to scan, and duration is a target length. It still omits units confirmation beyond the name, default values (90 minutes, the hardcoded default path), and what happens when folder is null.

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

Purpose4/5

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

The first sentence states a specific verb and resource ('Generate a focused study session') plus the inputs it draws from ('recurring concepts and likely gaps'), which hints that it composes the sibling concept-extraction and gap tools. It does not name those siblings explicitly, so an agent must infer the relationship rather than read it.

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

Usage Guidelines2/5

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

There is no statement of when to use this versus extract_concepts or get_learning_gaps, no prerequisites, and no exclusions. The phrase 'using recurring concepts and likely gaps' only implies a dependency on the sibling tools; nothing tells the agent which tool to pick for which request.

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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