creative_get_shelfRead one shelf hub as an object: its public URL, what it covers, what it deliberately does NOT cover (and which shelf owns each of those instead), and every article on it with status and live URL. Use it to check placement before drafting.
| Name | Required | Description | Default |
|---|---|---|---|
| shelf | Yes | One canonical Creative Library shelf. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description consistently says 'Read' without contradicting. The description adds value beyond annotations by detailing what the returned object contains (public URL, coverage, deliberate exclusions, articles with status and live URL) and the placement-checking purpose. It does not mention any edge cases or rate limits, but for a simple read, this is adequate.
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 dense sentences with no fluff. The first sentence front-loads the core action and lists the return payload in a structured way; the second gives the usage guidance. Every clause earns its place, making it efficient without losing clarity.
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 single-parameter, read-only tool with no output schema, the description fully covers what the agent needs: the input (shelf), the output content (URL, coverage, exclusions, articles with status/live URL), and the purpose (placement check before drafting). No additional details like pagination or error handling are necessary given the scope and annotations.
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?
The schema already provides 100% coverage: the 'shelf' parameter has an enum of 10 values with a description. The tool description only echoes 'one canonical Creative Library shelf' without adding new meaning or syntax details. Per the rubric, the baseline is 3 when schema covers parameters fully, and the description does not elevate it.
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 clearly states the verb 'Read' and the resource 'shelf hub', then enumerates the exact data returned (public URL, coverage, exclusions with owning shelves, articles with status and live URL). It differentiates this from sibling tools like creative_list_articles by specifying it returns the full hub object including exclusions and placement, not just a list.
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?
It gives a clear usage context: 'Use it to check placement before drafting.' This tells the agent when to call it but does not explicitly mention alternatives or when-not-to-use. Since the tool is for a very specific read action, the context is sufficient, though it could name sibling tools like creative_list_articles as alternatives.
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.
Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.
Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.
112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.
The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.