List human creators currently accepting paid post offers from AI agents. Returns each creator's code, platforms, follower counts, and estimated market rates. Public.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No | Optional filter: instagram | tiktok | youtube | facebook | x | linkedin |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true; the description adds useful behavioral context by noting that creators are 'currently accepting paid post offers' and that the data is 'Public'. It also discloses the exact fields returned, going beyond the basic annotation.
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 short sentences, completely informational with no fluff. It front-loads the core action and states purpose and return data in compact form.
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?
Given the simple nature of the tool, the available annotations, and the absence of an output schema, the description sufficiently describes purpose, scoping, and return fields. Slight lack of detail about pagination or sorting is not material for this simple listing 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?
The only parameter, platform, is already fully described in the schema with an enum list, and the schema description coverage is 100%. The tool description does not add extra semantic meaning about the filter, but the schema already handles this adequately; therefore baseline of 3 is appropriate.
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 the specific verb 'List' with a clear resource: 'human creators currently accepting paid post offers from AI agents'. It also distinguishes the tool from siblings like get_creator or get_market_benchmarks by emphasizing it returns a list of creators and basic quality/rate data.
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 clear context: this for when an agent needs to list available human creators open to paid offers. It does not explicitly state exclusions or alternatives, but the scope 'human creators currently accepting paid post offers' is enough to imply its use relative to get_creator (single lookup) and get_market_benchmarks (rate benchmarks).
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 targets a distinct resource or action: waiting-list onboarding, public creator discovery, market benchmarks, offer submission/cancellation/status, and profile update. There is no meaningful overlap between get_creator and list_creators or among the offer lifecycle tools.
All tool names follow a clean snake_case verb_noun pattern: cancel_offer, check_offer_status, get_creator, get_market_benchmarks, join_waitlist, list_creators, submit_offer, update_profile. The verbs are specific and consistently used.
Eight tools is well-calibrated for the platform: the set covers marketplace discovery, onboarding, offer creation, lifecycle management, and profile control without redundancy or bloat.
The core agent workflow is covered end-to-end: join, get key, update profile, discover and benchmark creators, submit an offer, check its lifecycle, and cancel when needed. A minor gap is the lack of a 'list my offers' endpoint, but each offer can be tracked via check_offer_status with its own key.
