Fetch Blender scene data in compact, standard, or detailed versions to match your AI model's token capacity. Choose the right size to avoid exceeding context limits.
Get scene info tailored to the model's context size.
Args: complexity: compact (~200 tokens for weak models), standard (~500 tokens for most models), detailed (~2000 tokens for large models).
| Name | Required | Description | Default |
|---|---|---|---|
| complexity | No | compact |
Changes observed during successful MCP inspections.
v3.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does explain the token-budget behavior for each complexity level, which is useful. However, it doesn't disclose what 'scene info' includes, whether the output is a summary or raw data, or any side effects (though it appears read-only). The token estimates add some transparency but leave the actual output structure unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence states the purpose, and the Args section immediately clarifies the parameter. Every sentence earns its place, and the token estimates are concise and actionable. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read-only tool with no output schema, the description covers the key decision an agent needs to make: which complexity level to choose. It doesn't describe the return format, but the tool's name and purpose imply a text/scene summary, and the token estimates partially cover output expectations. Given the low complexity, this is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does explain the single parameter 'complexity' with three concrete options and token ranges, which adds meaning beyond the bare schema. It doesn't specify the exact string values beyond the examples, but the examples are clear enough. This is strong compensation for a single-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get') and resource ('scene info') with a clear qualifier ('tailored to the model's context size'). It distinguishes itself from generic scene tools like get_scene_info and get_scene_summary by emphasizing context-size tailoring. However, it doesn't explicitly name a sibling alternative, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when a model needs scene info at a size appropriate for its context window. It also explains the three complexity levels and their token ranges, which helps an agent choose the right variant. It doesn't explicitly state when not to use it or name alternatives like get_scene_info, so it's not a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.