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suggest_next_step

Analyze the current Blender scene and get 1-3 concrete suggestions for the next logical step, based on common 3D workflows to keep your project moving forward.

Instructions

Analyze the current scene and suggest the next logical step.

Returns 1-3 concrete suggestions based on what's in the scene, what's missing, and common 3D workflows. Helps weak models stay on track without requiring them to plan ahead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool analyzes the scene and returns 1-3 suggestions based on scene contents, missing elements, and common workflows. However, it does not explicitly state that the tool has no side effects (e.g., it does not modify the scene), and it gives no information about prerequisites or potential limitations. This is adequate but not rich.

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, front-loaded with the core purpose and then providing detail on the output. Every sentence earns its place, with no filler or redundancy. It is appropriately sized for a tool with no parameters.

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?

The description is fairly complete for a simple suggestion tool. It explains what it does, what it returns, and the rationale for its use. However, it does not specify the exact format of the suggestions (e.g., are they command names, textual steps?) or whether a scene must be loaded. These are minor omissions given the low complexity and absence of parameters or output schema, so a 4 is apt.

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 tool has zero parameters, and the schema documents this fully (100% coverage). Since there are no parameters to explain, the description does not need to add anything. According to the calibration, a baseline of 4 is appropriate for 0 parameters. The description adds nothing about parameters because there are none.

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 states a specific purpose: 'Analyze the current scene and suggest the next logical step.' It clearly identifies the action (analyze and suggest) and the resource (current scene), and differentiates itself from sibling tools like get_scene_summary or auto_scene_report by focusing on suggesting a next step rather than just reporting information. The return of '1-3 concrete suggestions' makes its function unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: 'Helps weak models stay on track without requiring them to plan ahead.' This suggests when to use the tool (when the model is uncertain of the next step), but it does not explicitly mention alternatives or when not to use it. There is no explicit exclusion or comparison to other tools, so the 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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