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LLM chat completions: Llama 4 Scout

ai_chat
Read-only

Send a prompt, get an answer from Meta's Llama 4 Scout (17B active, 16 experts), with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Fast and cheap. Use for drafting, reasoning steps, Q&A and any quick LLM call without an AI provider account; other open models by name at /v1/chat/completions, harder reasoning at ai_chat_large. Example: prompt=Explain x402 in one sentence.. Price $0.002/call via x402 USDC or a card key (/credits), first 25 free; failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input Schema

NameRequiredDescriptionDefault
toolsNoOptional OpenAI tools array as JSON (function calling, up to 32 functions); the answer then carries tool_calls
promptYesUser prompt, up to 8,000 characters
systemNoOptional system prompt, up to 2,000 characters
messagesNoOptional OpenAI messages array as JSON (system, user, assistant with tool_calls, tool); replaces prompt and system. Together with tools, at most 20,000 characters
maxTokensNoMaximum output tokens, 1-1024 (default 400)
temperatureNo0-1.5 (default 0.6)
tool_choiceNoOptional: auto, none, required, or a JSON object naming one function
response_formatNoOptional OpenAI response_format as JSON: {"type":"json_object"} or a json_schema

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / messages
      Added value: +{
+  "description": "Optional OpenAI messages array as JSON (system, user, assistant with tool_calls, tool); replaces prompt and system. Together with tools, at most 20,000 characters",
+  "type": "string"
+}
    • addedInput schema / properties / response_format
      Added value: +{
+  "description": "Optional OpenAI response_format as JSON: {\"type\":\"json_object\"} or a json_schema",
+  "type": "string"
+}
    • addedInput schema / properties / tool_choice
      Added value: +{
+  "description": "Optional: auto, none, required, or a JSON object naming one function",
+  "type": "string"
+}
    • addedInput schema / properties / tools
      Added value: +{
+  "description": "Optional OpenAI tools array as JSON (function calling, up to 32 functions); the answer then carries tool_calls",
+  "type": "string"
+}
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/destructive annotations it discloses billing behavior that materially affects invocation strategy: $0.002/call via x402 USDC or a card key, first 25 free, failed calls free, and an explicit warning that repeats are not free (explaining idempotentHint=false). It also states no provider account is needed and that the call changes nothing.

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?

Purpose and free-tier/cost facts are front-loaded, and every sentence carries operational information. The single example ('prompt=Explain x402 in one sentence..') is truncated and adds little, and the tail end (pricing, read-only note) is densely packed into one run-on sentence.

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?

With 8 parameters, rich annotations and full schema coverage, the description covers cost, model identity and routing adequately. The one gap is the response shape: there is no output schema, yet the description never says what is returned (answer text plus optional tool_calls), which matters for the function-calling parameters.

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

Parameters3/5

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

Schema description coverage is 100%, so prompt, system, messages, tools, maxTokens, temperature, tool_choice and response_format are already fully documented. The description only echoes a few of these (optional system prompt, up to 1,024 output tokens, adjustable temperature), adding little beyond the schema; baseline 3 applies.

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

Purpose5/5

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

States a specific verb+resource ('send a prompt, get an answer') and pins the exact model (Llama 4 Scout, 17B active/16 experts) with its output limit and temperature range. It distinguishes itself from siblings by naming ai_chat_large for harder reasoning and pointing to /v1/chat/completions for other open models.

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

Usage Guidelines5/5

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

Gives explicit use cases (drafting, reasoning steps, Q&A, any quick LLM call with no provider account) and an explicit alternative with the condition that selects it ('harder reasoning at ai_chat_large'). An agent can route between this and its sibling without inferring anything.

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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