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batch_to_vault

Run multiple questions against a notebook and save each answer as a markdown file with YAML frontmatter and a structured JSON payload, creating a searchable vault for offline reference.

Instructions

Run a list of questions against a notebook and persist each answer to disk as two artifacts: {slug}.md (markdown with YAML frontmatter, answer body and cited source excerpts) and {slug}.json (structured payload conforming to the nblm-answer-v1 schema). Designed for one-shot ingestion of a notebook into a searchable markdown vault (e.g. for indexing with RTFM) — every answer keeps titles + highlighted excerpts, so repeat queries no longer need to round-trip through NotebookLM.

Reuses the same browser/session as ask_question — no HTTP server required. Pass sleep_between_ms (1500–3000ms) for batches above ~20 questions to avoid hammering NotebookLM.

Returns per-question file paths, success flags, citation counts and the resolved session id. See the RTFM integration guide for the recommended workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsYesNon-empty array of question strings to ask sequentially.
vault_dirYesDestination directory (absolute or relative). Created with mkdir -p if missing.
notebook_idNoOptional library notebook id to query. Falls back to the active notebook.
notebook_urlNoOptional NotebookLM URL (overrides notebook_id). Use for ad-hoc notebooks.
slug_prefixNoOptional filename prefix (e.g. "sota", "market-2026q2"). Default: "".
source_formatNoCitation extraction mode. "json" (default) preserves titles + excerpts in the sidecar.
sleep_between_msNoPause between questions in ms. 1500–3000 is sane for batches above ~20.
session_idNoOptional session id to reuse for context continuity across the batch.
Behavior3/5

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

With no annotations, the description carries full burden. It mentions session reuse and throttling but does not cover error behavior, prerequisites (e.g., authentication), or what happens on failure, leaving some behavioral gaps.

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 dense but not overly long, with the core purpose front-loaded. Every sentence adds relevant information, though a slight restructuring could improve scannability.

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

Completeness4/5

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

For a batch tool with 8 parameters, the description covers input, output (file paths, success flags), session reuse, and throttling. It lacks details on error handling but references the RTFM integration guide for further context.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying default behaviors (e.g., source_format default, mkdir -p for vault_dir) and providing sane ranges for sleep_between_ms, exceeding basic schema definitions.

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 it runs questions against a notebook and persists answers to disk as markdown and JSON artifacts. It distinguishes itself from siblings like ask_question by emphasizing one-shot ingestion and avoiding round-trips, providing specific verb+resource.

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 clear context: designed for one-shot vault ingestion and suggests sleep_between_ms for batches above ~20 to avoid throttling. It implies use as an alternative to repeated ask_question calls but does not explicitly list when to use versus other tools.

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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