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parse_meeting_notes

Turn meeting notes into actionable pack questions by parsing both common corpus formats, ready for use in asistonto's meeting copilot.

Instructions

Parse a corpus document into pack questions. Accepts both shapes the asistonto corpus uses: the four-part blockquote format and the older ### QC-1 <title> headings with numbered sub-questions. Reports which one it read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefixNoQC
sourceNo
markdownYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It usefully states that it accepts both format shapes and 'reports which one it read,' which is genuine behavioral detail. It does not mention error cases or explicitly reassure non-mutation, but 'parse' is naturally read-only and the output schema covers results.

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 exactly two sentences with no filler. The first sentence states the core purpose, and the second packs format variants and output behavior, making every word count.

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?

The description covers the primary input contract and output behavior, and an output schema exists to define return structure. The main gaps are the unexplained optional parameters and lack of error-handling details, but the tool is not overly complex, making this a largely complete description.

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?

The schema has no descriptions for markdown, prefix, or source, so the description must compensate. It explains the markdown parameter's accepted corpus formats well, and indirectly hints at the prefix parameter via the '### QC-1' example, but it leaves prefix and source underspecified and does not explain their roles or constraints.

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 opens with 'Parse a corpus document into pack questions,' which clearly names the verb, input, and output. It also enumerates the two accepted input shapes, distinguishing it from sibling authoring, validation, and writing tools.

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 clearly implies the appropriate context: use this when you have a corpus document in either supported format that needs conversion to pack questions. However, it does not explicitly discuss when not to use it or compare it with sibling tools like compose_question or write_pack.

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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