Get current fair-market rate benchmarks ($ per 1k followers by platform and deliverable kind), offer verdict rules (LOWBALL / FAIR / PREMIUM), and the marketplace's live combined reach (creators, connected accounts, total verified followers). Public.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds specificity about the data returned (fair-market benchmarks, verdict rules, reach metrics) and public accessibility. This extends the annotation by describing the tool's informational scope, though it does not mention response format or potential staleness.
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 a single dense sentence that front-loads the main purpose and packs the key output categories, verdict rules, and publicness without wasted words. Every clause adds value.
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 parameterless, read-only benchmark tool with no output schema, the description covers the returned data types and audience scope sufficiently. An agent can infer the tool is safe, public, and useful for pricing context before submitting offers.
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 tool has zero parameters, so the description carries no param burden. The baseline of 4 applies, and the description still gives useful context about what the no-parameter call will return.
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 names a specific verb ('Get') and resource ('market benchmarks'), and clearly enumerates the returned content: rate benchmarks per $1k followers by platform and deliverable kind, verdict rules (LOWBALL/FAIR/PREMIUM), and combined marketplace reach. This distinguishes it from sibling tools like submit_offer and check_offer_status.
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 this is a read-only reference tool that may be consulted before making offers, and states that it is public. It lacks explicit comparison to alternatives, but the context is clear enough for basic selection among siblings.
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.
