creative_publish_articlePublish only the exact reviewed draft revision. If the draft changed after review, publication fails and the new revision must be reviewed.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The reviewed draft slug. | |
| shelf | Yes | The reviewed draft shelf. | |
| expected_revision | Yes | The exact revision returned by creative_get_draft after human review. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: it explains that publication fails if the draft changed, requiring re-review. Annotations already mark it as destructive and idempotent, but the description clarifies the failure condition. No contradiction.
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 short, direct sentences with no wasted words. The key point is front-loaded: 'Publish only the exact reviewed draft revision.' All content is essential.
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 mutation tool with annotations providing safety info, the description is fairly complete. It explains the failure condition but does not mention the return value (e.g., success indicator). However, given low complexity, a small gap is acceptable.
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%, and the description does not add new information about parameters beyond what the schema descriptions already provide. The description aligns with the expected_revision parameter but adds no further meaning.
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 'Publish only the exact reviewed draft revision.' This specifies the action (publish) and the resource (draft revision). It distinguishes from sibling tools like creative_save_draft, which saves but doesn't publish.
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 includes a condition: 'If the draft changed after review, publication fails and the new revision must be reviewed.' This makes it clear when to use the tool (after review) and what happens if conditions aren't met. It does not explicitly name alternatives, but the condition provides sufficient guidance.
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.