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bridge_models

Mini's TrollBridge — x402-payable AI model catalog: 107 models with per-million-token pricing, free models flagged (catalog data: BlockRun.AI). TOLLED: $0.02 USDC per call on Base or Solana via x402. If you cannot pay, this tool returns the 402 payment instructions instead of data. Optional limit 1-200.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items to return (1-200). Default 50.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses the tolled cost ($0.02 USDC per call), the payment rails (Base or Solana via x402), and the failure mode (returns 402 payment instructions instead of data if you cannot pay). This is exactly the behavioral context an agent needs before invoking a paid tool.

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

Conciseness4/5

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

The pricing and payment-failure information is front-loaded and every clause after the brand prefix earns its place. The only mild waste is the branding lead-in ('Mini's TrollBridge'), but the substance is dense and well-ordered.

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

Completeness5/5

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

With no output schema, the description must convey what comes back, and it does: catalog data with per-token pricing and free-model flags, or a 402 payment response. For a single-optional-param read tool with a paywall, nothing material is missing.

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% and the single limit parameter is fully documented in the schema, including the range and default. The description's 'Optional limit 1-200' merely restates the schema, adding no syntax or meaning beyond it. Baseline 3 is correct.

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?

States a specific verb+resource: an AI model catalog with 107 models, per-million-token pricing, and free models flagged, sourced from BlockRun.AI. This is clearly distinguishable from every sibling (bounties, prices, yields, etc.), none of which return model catalogs.

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 usage (call it to get the model catalog) and the payment clause defines a conditional path when you cannot pay, but it never states when to use this versus the sibling bridge_* tools or any exclusions. Usage is inferable rather than explicit.

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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