get_sceneFetch one of your scenes (or any public scene) as JSON — edit it and re-save with update_scene, or feed it to generate_code. Requires an API key.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Scene id from list_scenes or an editor URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description is consistent with a read operation. It adds useful behavioral context beyond the annotation: the tool can access public scenes, returns JSON, requires an API key, and supports integration with update_scene and generate_code.
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?
Two compact sentences with no filler. The core purpose and output type are front-loaded, and the API key requirement and typical downstream uses are stated efficiently.
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 low-complexity read-only tool with one fully documented parameter, the description is largely sufficient: it states the return format, access scope, and prerequisite. It could mention error cases or whether the response has a particular structure, but no output schema exists and the essential usage information is present.
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 coverage for the single 'id' parameter is 100%, and the schema already explains the id's provenance (list_scenes or editor URL). The description adds no extra parameter semantics beyond what the schema provides, so the baseline 3 applies.
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?
Clearly states a specific verb ('Fetch') and resource ('one of your scenes or any public scene') with the return format (JSON). The use cases mentioned also distinguish it from list_scenes (single scene by id) and from update_scene/generate_code (which consume or modify the fetched result).
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?
Gives clear context: fetch a scene when you have its id and want JSON, either to edit or feed elsewhere. It notes the API key requirement, but does not explicitly exclude alternatives like list_scenes for enumeration or get_scene_format for format details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool maps to a distinct resource or action: scene CRUD, validation, motion sampling, recipe lookup, code generation, and QR preview. Potential pairs like validate_scene vs sample_scene are clearly separated as structural validation vs motion evaluation, and get_scene vs get_recipe differ by source and purpose.
Tool names consistently follow a verb_noun snake_case pattern: create_scene, get_scene, list_scenes, update_scene, validate_scene, search_recipes. The few compound nouns like get_preview_qr and get_scene_format still fit the same predictable convention.
11 tools is well within the ideal range and each tool serves a clear part of the scene authoring workflow: discovery, validation, sampling, editing, compiling, and previewing. None feel redundant or extraneous for the stated purpose.
The toolset covers the core scene lifecycle well: create, read, list, update, validate, sample, generate code, and preview. The main gap is the absence of a delete_scene tool, which is a minor omission since the authoring and iteration workflow is otherwise fully supported.