get_templateLoad one canonical company template by ID or public t-xxxx share code. Pass expected_template_kind when the caller requires a generation_preset or batch_workflow.
| Name | Required | Description | Default |
|---|---|---|---|
| share_code | No | Public t-xxxx share code | |
| template_id | No | Template ID | |
| expected_template_kind | No | Optional expected kind. A mismatch returns both expected and actual kinds. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds context about the template being 'canonical' and the share-code format logic, but it does not disclose additional behavioral traits like return structure, error cases, or authorization needs. Given the strong annotations, the description's extra value is modest; a 3 is appropriate.
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 two sentences, front-loaded with the core purpose, and contains no superfluous words. Every clause adds value: the load action, the identifier options, and the parameter guidance. It is exemplary in conciseness.
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 read tool with no output schema and only three optional parameters, the description covers the essential usage context. It states what it loads, how to identify the template, and when to pass the optional kind. It does not mention the return format, but given the tool's simplicity and the absence of an output schema, this is not a significant gap. Complete enough for an agent to effectively select and invoke it.
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 is 100% for all three parameters, each with a description. The description goes beyond the schema by explaining when to pass expected_template_kind ('when the caller requires a generation_preset or batch_workflow'), adding conditional context that the schema lacks. This enriches the parameter semantics beyond the baseline.
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 uses a specific verb ('Load'), identifies the resource ('one canonical company template'), and specifies the access methods ('by ID or public t-xxxx share code'). It clearly distinguishes this from sibling list_templates (which lists) and delete_template/save_template (which mutate). The mention of expected_template_kind further clarifies the tool's purpose for specific template kinds.
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 implies when to use this tool: when you need a single canonical template, as opposed to listing all templates. It provides explicit guidance for the expected_template_kind parameter ('Pass expected_template_kind when the caller requires a generation_preset or batch_workflow'). However, it does not explicitly name an alternative (e.g., 'for listing, use list_templates'), so it stops short of a full when/when-not explanation.
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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Most tools target distinct resources and have detailed descriptions, but several closely related families exist: create_credit_checkout_session vs mcp_create_credit_checkout_session, the propose_brief/confirm_brief/update_brief lifecycle, and the many avatar/upload entry points. An agent must read long caveats carefully to avoid selecting the wrong tool.
The vast majority of tool names follow a predictable snake_case verb_noun pattern (list_*, get_*, create_*, update_*, propose_*). The mcp_* prefix group and varied creation verbs (create/upload/save/add/generate) are minor deviations, though mcp_create_credit_checkout_session duplicating create_credit_checkout_session adds some confusion.
With 67 tools, this is an extreme mismatch by the rubric's own 50+ threshold, far beyond the typical 3-15 well-scoped range. Many tools are narrow lifecycle steps such as two-phase local uploads, app-only montage internals, and multiple ArtDirection authoring variants, making the agent-facing surface very heavy.
The core generation, brief, montage, and QA workflows are covered thoroughly with polling and result retrieval. However, notable lifecycle gaps exist: outfits, locations, avatars, and tags mostly have create/list/get but no update or delete, and delete_template is the only delete tool in the entire set.