Aayat AI

Chat ($0.01)

chat

Read-only

Pay-per-call LLM chat (Mistral Small 3.1 24B): Balanced multilingual model (140+ languages) for writing, reasoning and long inputs. No API key or account. POST JSON {"prompt": "..."} or OpenAI-style {"messages": [{"role": "user", "content": "..."}]}, optional "system", "max_tokens" (up to 1024), "temperature", "json": true. Up to about 20,700 characters of English in. Failed calls are not charged. Price: $0.01 in USDC per call (x402 or prepaid credits). 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.

Changed1 schema field changedSep 28, 2026
- changedInput schema / properties / prompt / maxLength
```
Previous value: -28000New value: +27600
```

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

A4/5.0

Behavior5/5

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

Annotations already establish read-only, open-world, non-idempotent behavior, and the description adds meaningful operational context beyond them: pay-per-call pricing ($0.01 USDC via x402 or prepaid credits), failed calls are not charged, no API key/account required, and free-trial availability. This is exactly the extra behavioral detail an agent needs before invoking a paid endpoint.

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?

A single dense paragraph that front-loads model identity and capability, then call format, limits, and pricing. Nearly every clause carries information, though the pricing/credits/trial tail is somewhat packed together rather than cleanly separated.

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

Completeness4/5

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

For a paid generation endpoint with an output schema and rich annotations, the description covers cost model, failure handling, input formats, and limits. Output format is delegated to the output schema as expected; only the absence of explicit sibling routing keeps it from a 5.

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 derived meaning the schema lacks: max_tokens caps at 1024, ~20,700 English characters input ceiling, both input formats accepted, and the optional json flag. It supplies context beyond raw field docs without fully restating every parameter's semantics.

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?

States a specific verb and resource ('Pay-per-call LLM chat') and pins the underlying model (Mistral Small 3.1 24B) plus its capability scope (multilingual, writing/reasoning, long inputs). That model/price positioning implicitly separates it from chat-fast and chat-premium, though no sibling is named outright, so it stops short of a 5.

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

Usage Guidelines3/5

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

It explains how to call the tool (POST a prompt or OpenAI-style messages, no API key needed) but gives no when-to-use / when-not-to-use guidance relative to chat-fast, chat-premium, or the other chat alternatives. Usage is implied only through the model and pricing description.

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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