ai_quote_sellQuote how much HYPE a given amount of $AI returns right now, net of the on-chain sell fee. Use it to preview a sell; it returns no transaction. To get the calldata, call ai_build_sell_tx with the same ai amount.
| Name | Required | Description | Default |
|---|---|---|---|
| ai | Yes | $AI amount as a decimal string, e.g. "1000" (max 1000000000) |
| Name | Required | Description | Default |
|---|---|---|---|
| hypeOut | Yes | ||
| disclosure | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "disclosure": {
+ "type": "string"
+ },
+ "hypeOut": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "hypeOut",
+ "disclosure"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only, idempotent, and non-destructive; the description adds the key behavioral facts that the quote is 'net of the on-chain sell fee', reflects the current market ('right now'), and returns no transaction. This goes beyond the structured hints and prevents confusion with the transaction builder.
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 sentences with no filler: the first states the core behavior and fee treatment, the second gives usage and the sibling alternative. The most important information is front-loaded.
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?
With one well-documented parameter, a rich output schema, and annotations covering safety, the description covers selection, invocation, and the distinction from the tx builder. Nothing an agent needs to call it correctly is missing.
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 input schema fully documents the single `ai` parameter, including type and maximum, so the description does not need to repeat it. It adds the useful cross-reference that the same `ai` amount is used for `ai_build_sell_tx`, but otherwise relies on the schema.
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 operation ('Quote how much HYPE ... returns right now') and a specific resource (a sell of $AI). It explicitly contrasts with ai_build_sell_tx by stating it 'returns no transaction', so an agent can distinguish it from the tx-building sibling.
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?
It explicitly says 'Use it to preview a sell' and directs the agent to 'call ai_build_sell_tx with the same ai amount' when calldata is needed. This gives both a positive use case and an alternative, with no ambiguity about when to choose it.
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.