io.github.Headdao/slidemaster-mcp

render_slides

Generate AI-created background images for presentation slides. Provide slide titles and descriptions to produce matching visuals for your project.

Instructions

Render AI-generated slide images for a project. Each slide gets a background image produced by the image generation model.

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>",
      "slides": "<slides>"
    },
    "name": "render_slides"
  }
}

Input Schema

Name	Required	Description
`slides`	Yes	Array of slide objects with title and description
`image_size`	No	Image resolution: '1K', '2K', or '4K' (default: '2K')
`project_id`	Yes	Target project ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.2Sep 6, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state that each slide gets a background image from the image generation model, but it fails to disclose whether rendering is asynchronous, whether it overwrites existing slide images, whether it triggers additional costs or rate limits, or whether the operation is reversible.

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?

Two concise sentences with no filler. The first sentence front-loads the verb and resource, and the second clarifies the per-slide output. Every clause earns its place.

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

Completeness2/5

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

The tool has no output schema and no annotations, so the description must be more informative. It leaves out return behavior, whether rendering is synchronous or asynchronous, and operational side effects. Given siblings like check_status exist, an agent would benefit from knowing whether this renders immediately or starts a background job.

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 input schema already covers 100% of the parameters with descriptions, so the baseline is 3. The description adds only a general tie to 'background image produced by the image generation model,' which reinforces the tool's purpose but does not clarify individual parameter syntax or behavior beyond the 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 clearly names the verb ('Render') and the resource ('AI-generated slide images for a project'), making it immediately obvious what the tool does. It also distinguishes itself from content-generation siblings like generate_script and generate_video by focusing specifically on producing slide background images.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to invoke render_slides relative to sibling tools such as generate_outline, generate_video, or check_status. There is no mention of prerequisites, sequencing in the workflow, or conditions that would make an alternative tool more appropriate.

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