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fareboxfun

farebox-mcp

by fareboxfun

list_models

Retrieve all available frontier LLM options with per-token USDC pricing on Solana, enabling you to compare models and select the right one for your MCP-compatible client.

Instructions

List all available Farebox models with pricing. Returns an array of model objects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return type ('Returns an array of model objects') which adds behavioral context. However, it doesn't disclose details like whether pricing is always included, ordering guarantees, pagination behavior, or failure states. For a read/list operation the safety profile is fairly evident from the name, but the description is minimal.

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 description is efficient at two sentences without filler. It front-loads the core purpose (list models with pricing) and adds the return type detail in the second sentence. It's appropriately sized for a zero-parameter list operation, though it could arguably be even more informative about the return structure in the same space.

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 simple list operation with zero parameters and no output schema, the description covers the essential information: what it lists (all Farebox models) and what's included (pricing). The return type is disclosed. Given the low complexity, the description is reasonably complete, though it could mention whether models are returned in a specific order or whether any filtering is possible.

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

Parameters4/5

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

The tool has 0 parameters, so schema description coverage is trivially 100%. With zero parameters, the description's job is minimal here. The description doesn't need to explain parameter details since there are none, earning a solid baseline score for this dimension.

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 what the tool does: lists all available Farebox models with pricing. The verb 'List' plus the specific resource 'available Farebox models' with the scope of including pricing is specific enough. It distinguishes from siblings like get_balance and get_usage which are clearly different resources, though it doesn't explicitly name alternatives.

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 this is the tool to call when you need to enumerate available models and their pricing. However, it doesn't explicitly state when to use this vs alternatives, nor does it mention any context in which it shouldn't be used. There's no explicit exclusion or alternative guidance given other than what the resource name naturally conveys.

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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