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haandol

ppt-generator

by haandol
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Python
Local

prepare_outline

Prepares the prompts and JSON schema needed to generate a structured slide outline. Requires confirmation of presentation purpose, audience, time, and presenter details before generating the outline.

Instructions

Prepares the prompt + JSON schema for the CLIENT to generate a slide outline.

This tool does NOT call an LLM. It normalizes inputs, creates/loads the project, saves the presentation metadata, and returns the system prompt, user prompt, and the JSON schema the outline must conform to. You (the client) then generate the outline JSON that follows response_schema, and pass it to ingest_outline.

IMPORTANT — Required checks before calling: Before calling this tool, you must ask the user to confirm the following items:

  1. Presentation purpose (purpose): e.g., "internal tech sharing", "customer proposal", "conference talk"

  2. Presentation time (presentation_minutes): how many minutes the presentation will be

  3. Audience type (audience_type): general/technical/executive

  4. Presenter info (presenter_name, presenter_title, presenter_org): presenter_org can be empty if not applicable. If the user has not explicitly provided these, never use default values — always ask.

Args: topic: Presentation topic (e.g., "2024 Cloud Computing Trends") purpose: Presentation purpose. Must confirm with the user before setting. audience_type: "general" | "technical" | "executive". Must confirm with the user. presentation_minutes: 3~60 min. Must confirm with the user. num_slides: Recommended number of slides (0 = auto-calculate from presentation time). presenter_name: Presenter's name. Must confirm with the user. presenter_title: Presenter's job title. Must confirm with the user. presenter_org: Presenter's organization (can be empty). Must confirm with the user. project_id: Project ID (auto-generated if not specified)

Returns: JSON string with: system_prompt, user_prompt, response_schema, project_id.

Next step: Generate the outline JSON matching response_schema, then call ingest_outline(project_id=<project_id>, outline_json=<your JSON>).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
purposeNo
num_slidesNo
project_idNo
audience_typeNogeneral
presenter_orgNo
presenter_nameNo
presenter_titleNo
presentation_minutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.8/5.0
Behavior4/5

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

With no annotations, the description fully explains what the tool does (normalize inputs, create/load project, save metadata, return prompts and schema) and what it does NOT do (LLM call). It could mention side effects like overwriting existing project data, but the detail is high.

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?

Description is well-structured with sections (main purpose, important notes, args, returns, next step). Despite length, every sentence adds value. Front-loaded with key 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 9 parameters (1 required), no annotations, and an output schema, the description covers workflow, user confirmations, output format, and next step. It leaves no ambiguity for the agent.

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

Parameters5/5

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

Schema has 0% description coverage, but the description provides detailed semantics for each parameter, including user confirmation requirements for key parameters (purpose, audience_type, presentation_minutes, etc.). This adds meaning far beyond the schema defaults.

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?

Description clearly states it prepares prompts and schema for outline generation, explicitly says it does NOT call an LLM, and distinguishes from sibling `ingest_outline` by specifying this tool is for preparation and the client generates the outline.

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?

Description provides explicit required checks before calling, listing four items to confirm with user. It also gives a clear next step: generate outline JSON and call ingest_outline. This guides the agent precisely on when and how to use the tool.

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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