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Glama

x402-services

by ai.firmbrain

ai_chat

LLM chat completion via x402 - no accounts, no API keys. Fast models (gpt-4o-mini, gemini-2.0-flash, llama-3.3-70b, mistral-small, deepseek-chat). OpenAI-compatible: send { messages:[{role,content}...], model?, maxTokens?, temperature? }. Payable in USDG on Robinhood Chain or USDC anywhere. Caps: 8k chars in / 1024 tokens out. [x402 paid tool — price $0.005; POST /api/ai/chat]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel or short name (default gpt-4o-mini)
messagesYesArray of {role, content} messages
maxTokensNoOutput cap, up to 1024
temperatureNo0-2, default 0.7

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description discloses payment ($0.005), input/output caps (8k chars / 1024 tokens), and model list. It omits failure behavior and latency but covers key traits.

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 paragraph with all essential info front-loaded. Every sentence adds value, including the concise metadata footer. No unnecessary words.

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?

Lacks explicit return format description (no output schema), but chat completion's output is standard. Missing error handling details, but adequate for a simple tool.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds significant value by explaining defaults (model, temperature), constraints (maxTokens up to 1024), and format (OpenAI-compatible messages).

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 clearly states it's an LLM chat completion via x402, lists models, and mentions no accounts/API keys. It distinguishes from generic tools but doesn't explicitly differentiate from sibling 'ai_chat_pro'.

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?

The description implies ease of use with no accounts/API keys, but does not provide explicit guidance on when to use this tool versus alternatives like 'ai_chat_pro' or 'ai_models'.

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