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RedHold

diagrams-so

Official
by RedHold

Generate a diagram

generate_diagram

Turn natural-language descriptions into cloud architecture diagrams with XML output, Well-Architected warnings, and score.

Instructions

Create a new cloud architecture diagram from a natural-language prompt. Returns the diagram id, its draw.io XML, Well-Architected warnings, score, and credits used. Costs credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesWhat to draw, e.g. 'AWS 3-tier web app with ALB, EC2 Auto Scaling and RDS Multi-AZ'
opinionatedNoApply best-practice hardening suggestions during generation (paid plans only).
diagram_typeNoarchitecture | flowchart | sequence | data_pipeline | ... (default: architecture)
cloud_providerNoaws | azure | gcp | kubernetes | oci | general (default: general)
Behavior4/5

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

Annotations already clarify readOnlyHint=false and destructiveHint=false. The description adds valuable non-obvious context: 'Costs credits' and the exact return payload (id, XML, warnings, score, credits used). It does not elaborate on persistence or side effects beyond generation, but this is sufficient given annotations cover the mutation/destruction profile.

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 two sentences, immediately front-loaded with the core action, and every phrase earns its place: what it creates, the input type, the returned data, and the cost. No filler or redundancy.

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?

Given the tool has 4 parameters (1 required) and no output schema, the description does a good job explaining the return format and cost. It does not explicitly state whether the diagram is saved/persisted (relevant for later retrieval via get/list tools), which is a minor gap. Overall, it conveys enough for an agent to invoke the tool and interpret the response.

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?

Schema description coverage is 100%, so all four parameters have meaningful descriptions within the schema itself. The tool description adds no additional parameter-level information beyond what the schema provides; it only frames the prompt as natural-language, which is already present. Baseline 3 applies.

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 states a specific action ('Create a new cloud architecture diagram from a natural-language prompt') and identifies the resource and method. It distinguishes itself from sibling tools like edit_diagram, update_diagram, and delete_diagram by emphasizing creation from a prompt, and it lists concrete outputs (diagram id, XML, warnings, score, credits used).

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?

The phrase 'Create a new' provides clear context that this tool is for initial generation, not modification or deletion. However, it does not explicitly name alternatives (e.g., 'use edit_diagram to modify'), so it lacks explicit exclusions. This fits a 'clear context, no exclusions' level.

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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