Aayat AI

Chat premium ($0.03)

chat-premium

Read-only

Pay-per-call LLM chat (Llama 3.3 70B): Strongest model for harder reasoning, coding and careful writing; supports JSON output. No API key or account. POST JSON {"prompt": "..."} or OpenAI-style {"messages": [{"role": "user", "content": "..."}]}, optional "system", "max_tokens" (up to 1536), "temperature", "json": true. Up to about 36,000 characters of English in. Failed calls are not charged. Price: $0.03 in USDC per call (x402 or prepaid credits). Paid only (not in the free trial).

Input Schema

TableJSON Schema

Name	Required	Description
`json`	No	Ask for JSON-only output.
`prompt`	No	A single user message (use this or messages).
`system`	No	System instructions.
`messages`	No	Conversation so far, OpenAI style: [{role, content}].
`max_tokens`	No	Most tokens to generate (default 512).
`temperature`	No	Randomness, 0-2.

Output Schema

TableJSON Schema

Name	Required	Description
`text`	Yes	The model's reply.
`model`	Yes	The model that answered.
`usage`	Yes
`fallbackFrom`	No	Only when the tier's own model was unavailable: the model you asked for (a stand-in answered).

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 28, 2026

addedOutput schema / properties / fallbackFrom

Added value: +{
+  "description": "Only when the tier's own model was unavailable: the model you asked for (a stand-in answered).",
+  "type": "string"
+}

addedOutput schema / properties / model / description
```
Added value: +"The model that answered."
```

First observedSep 28, 2026

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavioral context beyond annotations: pay-per-call pricing, no API key needed, failed calls not charged, payment methods (x402 or prepaid credits), input size limits, and supported request formats. It complements annotations (readOnlyHint, openWorldHint, idempotentHint) rather than contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well front-loaded with purpose and model identity, then request format and limits, ending with pricing. It is slightly long and repeats the price from the title, but every sentence carries useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a paid LLM chat tool with six optional parameters and a 100% covered schema, the description covers all critical aspects: purpose, request formats, pricing, failure handling, input limits, and what is not included. An output schema exists, so return value details are not needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds integration-level meaning: it shows a sample JSON body, mentions the 'messages' vs 'prompt' alternatives, and notes max_tokens is up to 1536. It adds useful context beyond the schema field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Pay-per-call LLM chat (Llama 3.3 70B)' and names its specialization for harder reasoning, coding, and careful writing. It implicitly distinguishes itself from the sibling 'chat' and 'chat-fast' by calling itself the strongest model and noting it is paid only, but it does not name those alternatives explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly indicates when to use this tool: for harder reasoning, coding, and careful writing, and notes that it is paid only (not in the free trial). However, it does not name an alternative for simpler or free tasks or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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