ppt-generator

Overview Schema Source Related Servers Discussions Admin

prepare_visual_qa_analysis

Prepares the vision analysis task for a single captured slide by returning the system prompt, user prompt, response schema, and screenshot image paths, so you can analyze a slide against its spec before ingesting results.

Instructions

Prepares the vision analysis task for ONE captured slide. No LLM call.

Returns the system prompt, user prompt, response_schema, and images (the screenshot path to read). Read the screenshot, analyze it against the spec, generate the analysis JSON matching response_schema, then call ingest_visual_qa_analysis. Analyze slides in parallel.

Args: project_id: Target project ID (required). slide_index: 1-based slide position. iteration: Iteration counter matching the capture (default 0). screenshot_path: Optional client-captured PNG path. When supplied, it is validated and used instead of requiring Playwright capture.

Returns: JSON with system_prompt, user_prompt, response_schema, images, project_id, slide_index.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>",
      "slide_index": "<slide_index>"
    },
    "name": "prepare_visual_qa_analysis"
  }
}

Input Schema

Name	Required	Description	Default
`iteration`	No
`project_id`	Yes
`slide_index`	Yes
`screenshot_path`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.8.2Nov 2, 2026
- addedInput schema / properties / screenshot_path
```
Added value: +{
+  "default": "",
+  "title": "Screenshot Path",
+  "type": "string"
+}
```
First observedv0.7.0Jul 10, 2026

TDQS

A4.8/5.0

Behavior4/5

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

With no annotations provided, the description does the important work of disclosing that this tool makes no LLM call, returns prompts/schema/images, and only prepares the downstream ingestion step. It also states that a supplied screenshot_path is validated and used instead of requiring Playwright capture. It does not cover failure behavior or idempotency, which keeps it from a 5.

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 front-loaded with purpose and critical workflow, then uses compact Args and Returns sections. Every sentence adds operational value, especially given the schema has no parameter descriptions.

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?

For a four-parameter preparation tool with an output schema, the description fully explains the sequence, the returned fields, and the role of each parameter. An agent has enough information to call it correctly and route the result to ingest_visual_qa_analysis.

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 description coverage is 0%, so the description must carry parameter meaning, and it does: project_id is the target project ID, slide_index is 1-based, iteration matches the capture with default 0, and screenshot_path is an optional client-captured PNG that is validated and used instead of Playwright capture. This adds substantial semantics beyond the bare schema.

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 states a specific verb and resource: it prepares the vision analysis task for exactly ONE captured slide, and explicitly notes that no LLM call is made. This clearly distinguishes it from sibling tools such as ingest_visual_qa_analysis and prepare_visual_qa_fix.

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?

It gives an explicit workflow: read the screenshot, analyze it against the spec, generate analysis JSON matching response_schema, then call ingest_visual_qa_analysis. It also says to analyze slides in parallel, so the agent knows both the sequence and the concurrency pattern.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.