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ppt-generator

ppt-generator

by haandol
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ingest_visual_qa_analysis

Validates client-generated visual QA analysis for a slide, reports issues and overall quality, and returns context for the fix step without applying fixes.

Instructions

Ingests the client-generated analysis: validate, report issues. No fix applied.

Call AFTER prepare_visual_qa_analysis and echo its prepared_context token. Missing or stale context is rejected; restart from prepare after upgrading an older client. If has_issues, pass analysis_context to prepare_visual_qa_fix.

Args: project_id: Target project ID (required). slide_index: 1-based slide position. analysis_json: The analysis JSON generated by the client. prepared_context: Opaque token from the same analysis prepare response.

Returns: JSON with has_issues, issues (dicts to feed into the fix step), overall_quality.

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>",
      "analysis_json": "<analysis_json>"
    },
    "name": "ingest_visual_qa_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_idYes
slide_indexYes
analysis_jsonYes
prepared_contextNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.2
    • addedInput schema / properties / prepared_context
      Added value: +{
+  "default": "",
+  "title": "Prepared Context",
+  "type": "string"
+}
  2. First observedv0.7.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose key traits: validation-only, no fix applied, rejection of missing/stale context tokens, and a client-version restart requirement. It does not say whether ingestion persists anything or what an error/partial-validation result looks like, which keeps it below 5.

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?

Front-loaded with purpose, then sequencing, then Args/Returns — no filler. However the line "pass analysis_context to prepare_visual_qa_fix" references a field not present in the stated Returns (has_issues, issues, overall_quality), which is a small structural inconsistency that could confuse the agent.

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?

An output schema exists, so restating return fields is redundant but harmless; the sequencing and rejection behavior round out the calling contract. What's missing is failure-mode detail (what validation errors look like, whether a rejected context consumes/advances anything) for a multi-step ingest tool.

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 0%, so the Args block is doing necessary work: it explains project_id target, 1-based slide_index semantics, and that prepared_context is an opaque token from the matching prepare response (including that omitting it is rejected). The weakness is analysis_json, described circularly as "the analysis JSON generated by the client" without any shape or content guidance.

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?

States a specific verb and resource ("Ingests the client-generated analysis") plus what is and is not done ("validate, report issues. No fix applied"). This clearly separates it from prepare_visual_qa_analysis (produce analysis) and prepare_visual_qa_fix (apply fixes).

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?

Explicit ordering: "Call AFTER prepare_visual_qa_analysis", "Missing or stale context is rejected; restart from prepare after upgrading an older client", and a conditional branch "If has_issues, pass ... to prepare_visual_qa_fix". An agent knows exactly when to call this versus the sibling prepare/fix 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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