bonsai-mcp

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ifc_save

Write an IFC model to a specified file path. Requires explicit overwrite permission to replace existing files, and optionally validates the saved file by reopening and checking it.

Instructions

Write the model to path.

A new path is required by default. Overwriting anything — including the file the model was opened from — needs overwrite=true, stated explicitly.

With validate=true the saved file is reopened and checked, and the result reports any issue that was not already in the model before saving.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "ifc_save"
  }
}

Input Schema

Name	Required	Description	Default
`path`	Yes
`validate`	No
`overwrite`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 1, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and meets it well. It explicitly states the overwrite-safe default, warns that even the origin file cannot be overwritten without overwrite=true, and explains the reopen-and-check behavior of validate=true.

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?

Three short paragraphs, each focused on a single concern: the action, the overwrite rule, and the validation behavior. There is no filler or repetition, and the most important information appears first.

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?

The description covers all three parameters and the meaningful behavioral edge cases for a save operation. An output schema exists, so return-value details do not need to be in the description. The combination of schema and description gives an agent everything needed to invoke the tool correctly.

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 description coverage is 0%, so the description must explain all parameters, and it does. Path is defined as the write target, overwrite is explained with a concrete overwrite condition, and validate is described in terms of reopening and checking. This goes far beyond the bare 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 specific verb and resource: 'Write the model to `path`.' This clearly identifies the action and distinguishes it from sibling tools like ifc_open or ifc_validate. The rest of the description builds on that core purpose without ambiguity.

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 gives clear practical guidance on when overwrite=true is required and what validate=true does. It does not explicitly compare against sibling tools, but the save-versus-open/validate distinction is self-evident and the parameter usage context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.