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get_gpu_comps

Read-only

falcon.sol: Listed USD per GPU-hour comps (RunPod, Modal, Lambda, CoreWeave public price pages) with source URLs, fetched_at, min/median/max and quote percentile. Costs $0.04 USDC (x402). [falcon.sol comps. Paid: $0.04 USDC per call via x402 (Base or Solana). Unpaid calls return x402 PaymentRequirements; pay with an x402-MCP client (_meta "x402/payment") and retry.]

Input Schema

NameRequiredDescriptionDefault
gpusNo
tiersNo
target_usd_per_gpu_hrNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior4/5

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

Annotations only say readOnly and openWorld; the description adds the crucial behavior that this is a paid call at $0.04 USDC on Base or Solana and that unpaid attempts fail with a payment challenge. It also discloses the shape of the payload (source URLs, fetched_at, min/median/max, quote percentile). The payment details are restated twice rather than expanded, which keeps it from 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?

Purpose is front-loaded in the first clause, which is good, but the payment terms appear twice ('Costs $0.04 USDC (x402)' and the bracketed repeat) and 'falcon.sol' is repeated. Roughly a third of the text is duplicated boilerplate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Payment/auth behavior and return fields are well covered, which matters for a paid endpoint with no output schema. What is missing is anything about the three optional inputs — an agent cannot know what values gpus and tiers accept or how target_usd_per_gpu_hr drives the percentile.

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

Parameters2/5

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

Schema coverage is 0%, so the description must carry all parameter meaning, and it carries none: gpus, tiers, and especially target_usd_per_gpu_hr (clearly tied to the 'quote percentile' output) are never explained. The agent must guess accepted GPU names, tier values, and the units/effect of the target price.

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 resource (listed USD per GPU-hour comps) with named sources (RunPod, Modal, Lambda, CoreWeave) and the aggregate stats returned, so the agent knows exactly what it gets. It does not distinguish itself from the sibling get_comps, which an agent could easily confuse it with.

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?

Gives a concrete procedural instruction for the unpaid path (unpaid calls return PaymentRequirements; pay with an x402-MCP client via _meta "x402/payment" and retry), which is genuinely actionable. However, it never says when to choose this over the sibling get_comps, or what preconditions (wallet balance) apply.

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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