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search_models

Search live LLM pricing database to find current token prices, context window, and category for any model or provider.

Instructions

Search the live LLM pricing database by model name, provider, or id. Returns matching models with current input/output prices (USD per 1M tokens), context window, modality, and category. Use this to answer 'how much does cost' or 'what models does offer'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 20, max 50).
queryNoFree-text match against model name, provider, or id (e.g. 'claude', 'gpt-5', 'gemini flash'). Omit to list all.
categoryNoFilter by category, e.g. flagship, reasoning, budget, coding, embedding, fast, mid-tier.
open_sourceNoIf true, only open-source/open-weight models; if false, only proprietary.
Behavior4/5

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

No annotations provided, so description carries full burden. It accurately describes the read-only search behavior and return fields. Could explicitly mention non-destructive nature but generally transparent.

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

Conciseness5/5

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

Two sentences, front-loaded with action and return, followed by usage examples. No fluff.

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?

With 4 parameters fully described in schema and return fields listed, description is complete. Could mention default limit but schema handles that.

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 coverage is 100%, baseline is 3. Description adds context like 'free-text match' and usage examples, but does not significantly extend schema descriptions.

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?

Description clearly states the tool searches a live pricing database by model name, provider, or id, and returns specific fields. It also gives example use cases that distinguish it from siblings like cheapest_models and compare_models.

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

Usage Guidelines4/5

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

Provides explicit usage scenarios: answering cost queries or listing models by provider. No explicit when-not-to-use, but the examples are strong guidance.

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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