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Agent Data Tools: AI text and media

by dev.workers.revmesh2074.agent-data-tools
OverviewDiscussionsAdmin

Chat completions (LLM): 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. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Adds real context beyond the annotations: cost ($0.002/call, first 25 free, failed calls free), that repeats are charged — which explains the idempotentHint=false annotation — and that it reads/computes only. It stops short of describing the response shape or latency, but the pricing and charge semantics are the non-obvious behaviors an agent needs.

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?

Front-loaded with the capability, then routing, then pricing, in a tight paragraph. It is dense but nearly every clause carries distinct information; the pricing clause is slightly run-on but earns its space given the non-idempotent billing behavior.

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 an 8-parameter, no-output-schema tool, the description covers model identity, limits, alternatives, cost and side-effect profile. The only real gap is what a successful call returns (answer string plus optional tool_calls) when tools/messages params are used.

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 coverage is 100% and each of the 8 parameters is documented in-schema with limits and defaults. The description only restates the system prompt, token cap and temperature, adding no syntactic or interaction detail beyond the schema, so the 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') with the exact backing model named, plus its size characteristics. It distinguishes itself from ai_chat_large and generic /v1/chat/completions endpoints in the same breath, so an agent can select it without opening any schema.

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?

Explicit when-to-use list (drafting, reasoning steps, Q&A, quick LLM calls without a provider account) and explicit routing to alternatives: harder reasoning → ai_chat_large, other open models → /v1/chat/completions. Includes a concrete example invocation.

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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