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x402-services

by ai.firmbrain

ai_chat_pro

Frontier-model chat completion via x402 (gpt-4o, claude-sonnet-4, gemini-2.5-pro, deepseek-r1, grok-3). OpenAI-compatible messages. Payable in USDG on Robinhood Chain or USDC anywhere. Caps: 6k chars in / 800 tokens out. [x402 paid tool — price $0.03; POST /api/ai/chat-pro]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel or short name (default gpt-4o)
messagesYesArray of {role, content} messages
maxTokensNoOutput cap, up to 800
temperatureNo0-2, default 0.7

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, so description carries burden. Discloses payment method, character/token caps, price, and model selection. Lacks error handling or failure modes, but adequately informs about constraints.

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

Conciseness5/5

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

Single sentence covering purpose, models, compatibility, payment, caps, and price. No wasted words; front-loaded with key information.

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?

No output schema, but return format is standard for chat completions. Covers payment, caps, and models. Minor gap: no description of response structure.

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% (baseline 3). Description adds 'OpenAI-compatible messages' clarifying format, and mentions default model and caps. Does not elaborate on temperature range or maxTokens beyond schema.

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?

Description clearly states it's a 'Frontier-model chat completion' tool listing specific models, OpenAI compatibility, payment details, and caps. Distinguishes from sibling 'ai_chat' (likely simpler/free) through explicit features.

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?

Provides explicit context: supported models, payment requirements (USDG on Robinhood Chain or USDC), output caps, and price ($0.03). Does not directly compare with siblings, but sibling list includes 'ai_chat' implying alternative.

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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TDQS

A3.5/5.0
Disambiguation4/5

Tools are organized by domain prefix (e.g., 'crypto_', 'rh_', 'snipe_'), which helps distinguish between areas. Within each domain, they serve distinct purposes, though some overlap between domains exists (e.g., price data appears in multiple groups). Overall, an agent can navigate effectively.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a domain prefix and a verb_noun combination (e.g., 'compliance_risk', 'rh_stock', 'snipe_honeypot'). This makes the API predictable and easy to explore.

Tool Count2/5

With 159 tools, the server is extremely large. While the broad scope of web3 and utility functions justifies many tools, the count is significantly above the typical range for a coherent toolkit, potentially overwhelming agents and increasing selection error.

Completeness4/5

The toolkit covers a wide range of web3 operations: crypto, DeFi, compliance, safety, scheduling, memory, etc. There are no obvious major gaps for its intended purpose, though some niche areas might be missing.

Resources