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generate_diagram

Convert text prompts into visual architecture diagrams. Select diagram type, theme, resolution, and more.

Instructions

Generate an architecture diagram from a text prompt.

Args: prompt: Description of what to generate diagram_type: Type of diagram (architecture|data_flow|component|sequence|integration|infographic|c4_container|exec_infographic|generic) theme: Background theme (light|dark). Default: light — the portfolio-wide default (rep / marketing / CISO-facing output is the common case). Pass theme="dark" for a dark charcoal canvas. This single switch governs the background; you do NOT need to describe a background color in prompt. provider: LEAVE AS DEFAULT ("auto"). The server is responsible for picking the right provider and model for the diagram type — callers should describe what to draw and let the server decide how. The current default chain is OpenAI gpt-image-2 (primary) → Gemini (fallback). Override only if you have a specific provider/model comparison need. model: LEAVE UNSET unless you're explicitly benchmarking models. Server picks the right model for the chosen provider. resolution: Output resolution (1K|2K|4K) aspect_ratio: Output aspect ratio (16:9|1:1|9:16|4:3) style_reference: Style name or path to reference image output_path: Where to save the image (auto-generated if not provided) temperature: Generation creativity (0.0 to 2.0) quality: Output quality tier for OpenAI gpt-image-2 / gpt-image-1-mini (low|medium|high|auto). Cost scales dramatically: at 1536x1024 on gpt-image-2, low=$0.005, medium=$0.041, high=$0.165. Ignored by Gemini and legacy gpt-image-1.5. Default: auto.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
themeNolight
promptYes
qualityNoauto
providerNoauto
resolutionNo2K
output_pathNo
temperatureNo
aspect_ratioNo16:9
diagram_typeNogeneric
style_referenceNo
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden, and it does this well: it explains default provider fallback, cost scaling by quality tier, and that theme governs only the background. It stops short of stating file-creation side effects or auth requirements, but the output_path parameter and cost details provide meaningful transparency.

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 one-line summary is followed by a well-organized Args block. While long, each line adds essential info about defaults, constraints, or pricing; no filler or repetition. It is appropriately detailed for an 11-parameter tool.

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 11 parameters, no annotations, and no output schema, the description is remarkably complete. It covers all parameters, provides usage decision rules, cost implications, and fallback behavior, leaving little ambiguity for an agent invoking the tool.

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 coverage is 0%, so the description is the only source of parameter meaning. Every parameter is explained in Args, including defaults, allowed values, and special guidance for provider/model (LEAVE AS DEFAULT) and quality (cost breakdown). This far exceeds mere schema names.

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 a clear verb and object: 'Generate an architecture diagram from a text prompt.' It distinguishes itself from sibling 'edit_diagram' by focusing on creation, and the parameter list covers many diagram types, making the scope explicit.

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?

Provides strong usage guidance: tells callers to leave provider/model as default, explains when to override, and gives context on theme defaults. It does not explicitly name alternative tools like list_templates, but the generation-vs-edit distinction is clear enough for selection.

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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