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Agent Daily

by io.github.gregpunch
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Live LLM leaderboard

llm_leaderboard
Read-onlyIdempotent

Which LLMs are up, correct and fast right now. Every 15 minutes ~20 popular models (OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, xAI, Qwen, Cohere...) are asked to reply 'pong'; returns rank, score (60% 24h correctness + 40% speed), correct %, p50 time-to-first-token, p50 total time, cost per 1k calls, uptime and last result. Use to pick a fast, reliable model. sort: score (default) | correct | ttft | total | cost.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "llm_leaderboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
sortNo
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the read-only/idempotent annotations, it discloses refresh cadence (~15 minutes), provider sample, probing method, ranking formula (60% correctness + 40% speed), and returned metrics. It omits details like rate limits or auth needs, but the behavioral profile is substantially richer than the annotations alone.

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?

The description is front-loaded with the core purpose and then efficiently packs refresh cadence, return fields, scoring formula, and sort options. Every sentence adds useful selection or interpretation value.

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 no output schema, the description appropriately lists the returned fields and scoring formula. It is nearly complete for this tool, though the missing 'limit' parameter explanation leaves a small gap.

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 0%, so the description must carry parameter meaning. It explains the sort enum values and default ('score'), including what ttft/total/cost refer to, but it does not mention the 'limit' parameter at all.

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?

It states a specific resource and scope: a live leaderboard of popular LLM providers, refreshed every 15 minutes via a synthetic 'pong' check. The description makes clear this is a model-performance ranking tool, not an incidents/tool-status/recommendation tool, so it is distinguishable from siblings without opening schemas.

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?

It gives clear usage context: 'Use to pick a fast, reliable model,' and explains sorting choices. It does not explicitly name alternatives or when-not-to-use conditions, but the intended selection 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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