pricing_info
Return pricing details for the GoCreative Agent API — base price per call, premium endpoints, cache TTLs, and supported payment networks. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Return pricing details for the GoCreative Agent API — base price per call, premium endpoints, cache TTLs, and supported payment networks. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It indicates the tool is read-only by stating it 'returns' pricing details and adds 'Free' to signal no cost. However, it could more explicitly confirm no side effects or rate limits.
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 sentences long, starting with the main action and listing key outputs. Every word adds value; no redundancy. It is ideally sized for a simple info tool.
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 tool's simplicity (no parameters, no output schema), the description covers the essential information an agent needs. It could be slightly more complete by hinting at the return format (e.g., JSON), but it is sufficient for most use cases.
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 baseline is 4. The description does not need to add parameter meaning, and the schema coverage is 100% (no parameters to document). The description adds no param info, which 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 clearly states the tool returns pricing details for the GoCreative Agent API, listing specific components (base price per call, premium endpoints, cache TTLs, supported payment networks). This distinguishes it from sibling tools like list_endpoints or usage_stats, which serve different purposes.
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 usage for retrieving pricing information but does not explicitly state when to use this tool over alternatives or mention any prerequisites. The context (siblings) suggests it's the go-to for pricing, but no direct guidance is given.
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 occupy distinct roles (install, usage stats, wallet setup), but list_endpoints and pricing_info overlap substantially: both report paid endpoints and prices, even though one emphasizes live status and the other detailed fees/payment networks. Descriptions help separate them, but an agent could still pick the wrong one for a pricing question.
Names are uniformly snake_case and readable, but the pattern is mixed: install_snippets and list_endpoints use verb_noun, while pricing_info, usage_stats, and wallet_helper use noun-based names. This is a readable mixed convention rather than a chaotic one.
Five tools is a reasonable number for a small server, but for a product called 'Prediction Market Data for AI Agents' the set is oddly scoped: everything is onboarding/pricing/usage meta-tools rather than data tools. The count is not excessive, but it feels thin for the apparent market-data purpose.
The server promises prediction market data, yet none of the five tools retrieves markets, odds, prices, positions, or any actual market data. Even if the free helper tools are complete, the core data surface is entirely absent, leaving agents with no way to fulfill the server's stated purpose.