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Flat-rate LLM

llm_completion

LLM inference at a flat $0.005 per call, with no account, no API key and no token metering. POST a prompt, get the completion: the same price for 10 tokens or 4000, while metered gateways scale with usage. Automatic failover across several large models; typical response under 1s. Pay per call in USDC over x402 - nothing to sign up for. $0.005 per call, paid over x402 (USDC).

Input Schema

NameRequiredDescriptionDefault
promptYesThe request for the model. Up to 24000 characters.
sistemaNoOptional system instruction.
maxTokensNoOutput token cap, up to 4000. Does not change the price.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the payment mechanism (USDC over x402), that no account or API key is needed, automatic failover across several models, and typical sub-1s latency. It does not describe failure behavior when failover is exhausted or the response shape, so it falls short of a 5.

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

Conciseness3/5

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

The price is asserted three times ('flat $0.005 per call', '$0.005 per call, paid over x402', plus the title) and the 'no account/no API key' point is restated. The pricing hook is front-loaded, but roughly a third of the text is redundant.

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 a paid inference call with no output schema and no annotations, the description covers the unusual essentials an agent must know: cost model, auth (none), payment rail, failover, and latency. Only the return format and error semantics are left unstated.

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 the schema already documents prompt, sistema, and maxTokens. The description adds only a marginal hint that token count does not change price, which the schema's maxTokens note already states. Baseline 3 is appropriate.

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?

States a specific verb and resource: 'POST a prompt, get the completion', plus the differentiating flat-rate pricing model. It does not name or contrast with the sibling chat_completions, so it is clear but not sibling-differentiated.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to pick this over chat_completions or the many content_* generation tools. The only contrast offered is against unnamed 'metered gateways', which is a market comparison, not a routing rule for the agent.

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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