Declare that one or more AI tools were used to create a post. Pass tool_slug (singular) or tool_slugs (plural). Returns the merged tool_ids plus added_slugs and unknown_slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| post_id | Yes | Post ID | |
| tool_slug | No | AI tool slug (singular) | |
| tool_slugs | No | Array of AI tool slugs (plural form) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by explaining the return payload ('merged tool_ids plus added_slugs and unknown_slugs') and the singular/plural calling pattern. It implicitly frames the operation as an additive merge, which is consistent with idempotentHint=true and destructiveHint=false. No contradiction exists between description and annotations.
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 concise sentences communicate the core purpose, parameter alternatives, and return format with no filler. The most important distinction (singular vs. plural) is front-loaded and easy to parse.
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 three-parameter tool with no output schema, the description covers the key contextual details: what the call does, which parameters are expected, and what is returned. It does not document error handling or the exact meaning of 'added_slugs' versus 'unknown_slugs', but those gaps are minor given the simplicity of the operation.
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?
Parameter schema coverage is 100%, so the schema already documents each parameter's meaning. The description adds marginal value by clarifying the singular/plural choice and the merge behavior, but most of the parameter-level semantics remain with the schema. This is the baseline expected for fully covered schemas.
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 action ('Declare') and the resource ('that one or more AI tools were used to create a post'), which unambiguously conveys the tool's role. It doesn't explicitly differentiate from siblings, but no sibling overlaps with this declaration-of-tool-usage function, so the purpose is readily distinguishable.
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 no when-to-use guidance or comparison with alternatives. It gives usage hints ('Pass tool_slug or tool_slugs') but does not explain under what circumstances this tool should be chosen over another or when it should not be used. This leaves selection context almost entirely to the agent's inference.
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.
Most tools clearly target distinct resources and actions, but there is some overlap: get_profile and get_my_earnings both expose balance/cap information, and list_my_profiles could be confused with get_profile. The descriptions are generally clear enough to avoid serious misselection.
Every tool name follows a consistent snake_case verb_noun pattern: get_product, list_campaigns, submit_post, update_profile, etc. The verbs match the intended actions, and there are no mixed naming conventions or vague style deviations.
Twenty tools is slightly heavy, but the count is reasonable for the scope: the server covers products, campaigns, posts, earnings, profiles, notifications, asset uploads, and AI tool usage/reviews. Each tool serves a recognizable purpose, so the size feels like breadth rather than redundancy.
Core workflows are present—search products, get campaigns, apply, generate tracking links, submit posts, view earnings, manage notifications—but there are notable gaps. There is no way to list past posts or retrieve a post_id except from submit_post, no list of campaign applications, and no tool discovery endpoint for the AI tool slugs used by declare_tool_usage and write_tool_review.
