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Bonsai MCP

Bonsai MCP

by Show2Instruct
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_psets

Read-only

Retrieve property sets and quantity sets for IFC objects by global IDs or names, with results returned in input order.

Instructions

[QUERY] Return IFC property sets (psets) and quantity sets (qtos) for one or more objects. Accepts any mix of global_ids and names lists (Blender object names). Up to 100 targets per call. The response preserves input order in a results list.

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_psets"
  }
}

Input Schema

NameRequiredDescriptionDefault
namesNo
global_idsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by noting the response preserves input order in a results list and the 100-target limit, providing behavioral context beyond annotations.

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 three sentences with no wasted words. It is front-loaded with the core purpose and efficiently conveys all key information.

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's simplicity and lack of output schema, the description covers purpose, parameters, limits, and response order. It might lack details on the exact format of returned data, but it is largely complete for selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage. The description compensates by explaining that names are Blender object names and that global_ids are accepted in any mix, along with the limit. This adds meaning beyond the raw 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 states the tool returns IFC property sets and quantity sets for objects. It specifies the resource and action, and the sibling tools include other getters, making it distinct.

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 description starts with [QUERY] indicating a query tool, and specifies acceptable inputs (global_ids and names lists) and a limit of 100 targets. It lacks explicit alternatives or when-not-to-use, but the context is clear.

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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