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by io.github.abelce
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Get Shape Catalog

get_shape_catalog

Return compact machine-readable shape metadata for planning a diagram: semantic tags, geometry family, container support, recommended padding, default size, and MCP availability. Use this before create_diagram when choosing shapes or containers. Unlike get_shape_docs, this tool is concise and filterable.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_shape_catalog"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagNoFilter by one exact AI tag, e.g. container-capable, decision, database, or architecture.
categoryNoFilter by the shape category.
containerModeNoFilter by container support. adaptive shapes become containers when they receive children.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral disclosure burden. It clearly signals a read-only catalog lookup with 'Return...' and lists the output fields: semantic tags, geometry family, container support, recommended padding, default size, and MCP availability. It stops short of describing exact response formatting or edge cases, so it isn't a 5.

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 focused sentences carry all essential information: what the tool returns, when to use it, and how it differs from a sibling. 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 there is no output schema and no annotations, the description compensates well by naming the output fields and giving usage context. It could be slightly more explicit about the response envelope or how 'MCP availability' is represented, but overall it is complete enough for safe invocation.

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%, and each parameter already has a clear description, including enum meanings. The tool description only adds that the result is 'filterable,' which is consistent but not a major semantic addition. Baseline 3 is appropriate.

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 uses a specific verb and resource: 'Return compact machine-readable shape metadata for planning a diagram.' It enumerates the exact content returned and explicitly contrasts itself with get_shape_docs, making it easy to distinguish from siblings.

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?

It gives explicit timing and context: 'Use this before create_diagram when choosing shapes or containers.' It also names the alternative tool, get_shape_docs, and explains the difference, so an agent knows when to pick this tool.

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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