Paid $0.002 (x402, USDC Base): LLM chat completion (Llama 3.1 8B, Cloudflare Workers AI). Args: prompt (string) OR messages (array of {role, content}), optional system, max_tokens. One-shot AI inference per call, no signup.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | No | One-shot prompt | |
| system | No | System prompt (optional) | |
| messages | No | OpenAI-style [{role, content}] chat history | |
| max_tokens | No | Max completion tokens, default 512 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does notable work: it discloses the price ($0.002), the payment rail (x402, USDC Base), the no-signup property, and the one-shot nature. It omits rate limits, failure behavior, and output shape, but the payment/auth context is genuinely useful disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences, front-loaded with the cost/payment model and capability, then the argument contract. Every clause carries information; nothing is padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 4-param, no-annotation, no-output-schema tool it covers cost, model, invocation modes, and constraints adequately. It does not explain what a successful response looks like or what happens if neither prompt nor messages is supplied, which are the only meaningful gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is already 100%, so baseline is 3, and the description adds the non-obvious relationship that 'prompt' and 'messages' are alternative input modes rather than cumulative. 'optional system, max_tokens' reinforces the optionality already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('LLM chat completion') plus the exact model and provider (Llama 3.1 8B, Cloudflare Workers AI). It is unmistakably distinct from every sibling, all of which are crypto/market/data utilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It notes the two input modes (prompt OR messages) and that it is 'one-shot AI inference per call, no signup,' which implies usage, but it never states when to prefer one mode over the other or when this tool is the right choice. No exclusions are given, and no sibling competes for the same job.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.