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Ping AI models in parallel and get ranked results by latency. Filter by tier, provider, or free models. Optionally verify models with a real prompt to detect broken ones.

Instructions

Ping models in parallel and return ranked results by latency. Use when you need live speed data or a ranked list.

Pings all matching models, returns sorted fastest-first. Takes 2-10 seconds depending on filters.

When verify=True, sends a real prompt to each "up" model and checks for non-empty content. Models that return empty/garbage are marked as BROKEN (distinct from ERROR or OVERLOADED). This catches models that ping as UP but are functionally dead. Verification results are cached across scans within the session.

Args: tier: Filter to exact tier (S+, S, A+, A, A-, B+, B, C) provider: Filter to provider key (nvidia, groq, cerebras, etc.) min_tier: Show this tier and above (e.g. "S" shows only S+ and S) configured_only: Only ping models whose provider has an API key free_only: Only include models marked as free (from API or :free/-free in id) limit: Max results (default 20, 0 = all) verify: Send a real prompt to validate non-empty content (default false) verify_prompt: Custom verification prompt (default "Reply with exactly: OK")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
limitNo
verifyNo
min_tierNo
providerNo
free_onlyNo
verify_promptNo
configured_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully explains the ping-and-sort behavior, verification logic, caching, and what BROKEN status means. It lacks details on error handling or rate limits, but covers core behavior well.

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 well-structured with a lead-in, detailed verification note, and bulleted args. It is somewhat lengthy but every sentence adds value; could be slightly more concise.

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?

Given the 8 parameters and existence of an output schema, the description covers the core functionality, return order, verification behavior, and caching. It does not explain the output structure beyond 'fastest-first', but the output schema likely covers that.

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

Parameters5/5

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

The description provides comprehensive explanations for all 8 parameters, including examples and default values, compensating for the 0% schema description coverage. Each parameter's purpose and valid values are clearly stated.

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?

The description clearly states 'Ping models in parallel and return ranked results by latency' with a specific verb and resource. It also distinguishes from siblings like 'get_fastest' by mentioning 'ranked list' and 'Use when you need live speed data or a ranked list.'

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?

The description says 'Use when you need live speed data or a ranked list' and notes the time range 'Takes 2-10 seconds depending on filters.' It does not explicitly mention when not to use or alternatives like 'get_fastest', but the context is clear.

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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