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QuassLabs API

describe_diagram

Turn a plain-English description into Mermaid diagram source. Describe the flow, system, schema or timeline you want ("a login flow with an OAuth branch and a failure path") and get back complete, valid Mermaid - no need to know the syntax. The mermaid field of the response is directly usable as the mermaid_source input of convert_diagram, so describe -> render is a two-call pipeline. Generation runs on our infrastructure via AWS Bedrock; your description is not sent to any third-party service beyond that.

Input Schema

NameRequiredDescriptionDefault
descriptionYesWhat the diagram should show, in plain language. Include the nodes/steps/entities you care about; the generator fills in sensible structure.
diagram_typeNoForce a specific Mermaid diagram type. Default "auto" - the generator picks the type that fits the description.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations present, the description carries the full behavioral burden; it does disclose the generation backend (AWS Bedrock) and a data-handling guarantee, which is real added value. It omits error behavior for unparseable descriptions, latency/rate-limit expectations, and whether output is guaranteed valid Mermaid beyond the claim of 'complete, valid' source.

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 the core action, and the example plus pipeline note earn their space. The closing sentence about AWS Bedrock is tangential to invocation but buys legitimate transparency, so it is defensible rather than wasteful.

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?

For a two-parameter generation tool with no output schema, the description covers input style, diagram-type behavior, and the return value's usable field. Only streaming/error semantics for failed generation are left unaddressed.

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 both parameters, including the full diagram_type enum, are already documented in the schema. The description's example ('a login flow with an OAuth branch and a failure path') illustrates the description parameter's style but adds no rule beyond what the schema states.

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: turn a plain-English description into Mermaid diagram source. It also distinguishes itself from the sibling convert_diagram by framing the pair as a describe -> render pipeline, so an agent can pick the right one without opening the schemas.

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?

Gives a concrete input example and explicitly routes to convert_diagram for the rendering step. It does not spell out the inverse case (if you already have Mermaid source, skip this and call convert_diagram directly), so the when-not guidance is only implied.

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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