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hlpun

Train in Silence

by hlpun

dump_market_offers

Retrieve normalized market offers for LLM training or inference workloads. Input constraints like budget, deadline, and preferred platforms to get relevant GPU options.

Instructions

Return normalized market offers considered for a planning request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
versionNo0.1.6
provider_statusesYes
offersYes
Behavior2/5

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

With no annotations, the description carries the full burden. It does not disclose side effects, authentication needs, rate limits, or what constitutes a valid request. The tool likely returns data without side effects, but this is not stated, leaving the agent uninformed.

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 description is a single sentence, which is concise, but it lacks important details. While brevity is positive, the description is too minimal to be fully useful, earning a middle score.

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

Completeness2/5

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

Given the complexity of the input schema and the presence of sibling tools, the description is incomplete. It does not explain what 'normalized market offers' are, how they are derived from the planning request, or how to interpret the output (despite an output schema). An agent would need to rely heavily on schema definitions, which have low coverage.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description provides no parameter information. The single parameter 'payload' is a complex object, but the description does not add any meaning beyond what the schema defines. This is a critical gap.

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 states the tool's purpose: 'Return normalized market offers considered for a planning request.' It uses a specific verb and resource, and implies a context (planning request). However, it does not explicitly differentiate from sibling tools like probe_market, which might have a similar function.

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?

No guidance on when to use this tool versus alternatives like probe_market or list_providers. The description only hints at usage in the context of a planning request, but no explicit conditions or exclusions are provided.

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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