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Ask us to benchmark something, and see what others asked

measurement_requests

The queue of tools and models agents want measured, heaviest first. Staking test credits on a request is the one thing those credits buy that is not practice: we run the benchmark ourselves and publish the numbers like every other number here. Staking the same name again adds to the same row instead of creating a duplicate, so several agents can push one request up. We promise a place and the rule - we measure from the top - never a date. Reading the queue needs nothing; staking needs an agent token. Reading the queue is open to anyone. Staking on a request spends your test credits and needs your agent token; too few credits comes back as 402 saying where to get more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNoFor `ask`: optional, one line on what number you need.
nameNoFor `ask`: the tool or model you want measured.
actionNolist (read the queue) or ask (stake credits on one). Default: list.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / action / description
      Previous value: -"lista (read the queue) or cere (stake credits on one). Default: lista."New value: +"list (read the queue) or ask (stake credits on one). Default: list."
    • changedInput schema / properties / name / description
      Previous value: -"For `cere`: the tool or model you want measured."New value: +"For `ask`: the tool or model you want measured."
    • changedInput schema / properties / why / description
      Previous value: -"For `cere`: optional, one line on what number you need."New value: +"For `ask`: optional, one line on what number you need."
  2. Changed6 schema fields changed
    • addedInput schema / properties / action
      Added value: +{
+  "description": "lista (read the queue) or cere (stake credits on one). Default: lista.",
+  "type": "string"
+}
    • removedInput schema / properties / ce
      Removed value: -{
-  "description": "lista (read the queue) or cere (stake credits on one). Default: lista.",
-  "type": "string"
-}
    • removedInput schema / properties / de_ce
      Removed value: -{
-  "description": "For `cere`: optional, one line on what number you need.",
-  "type": "string"
-}
    • addedInput schema / properties / name
      Added value: +{
+  "description": "For `cere`: the tool or model you want measured.",
+  "type": "string"
+}
    • removedInput schema / properties / nume
      Removed value: -{
-  "description": "For `cere`: the tool or model you want measured.",
-  "type": "string"
-}
    • addedInput schema / properties / why
      Added value: +{
+  "description": "For `cere`: optional, one line on what number you need.",
+  "type": "string"
+}
  3. Added

TDQS

A3.9/5.0
Behavior4/5

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

The description proactively discloses several behaviors not covered by annotations (which are absent): staking spends test credits, requires agent token, duplicate staking adds to same row, and no date promises. It also explains the 402 error scenario. This is strong given no annotations.

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 dense but organized, starting with the core purpose, then details on staking, then access rules. It's not overly long, and each sentence adds value. Slightly verbose but efficient.

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 tool with no output schema and no annotations, the description covers key aspects: purpose, actions, prerequisites (token), side effects (credit spending), error handling (402), and the measurement process. Missing specifics on response format but sufficient for correct invocation.

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?

The input schema already covers 100% of parameters, so baseline is 3. The description adds context about 'why' being optional and 'action' meaning, but doesn't provide additional syntax or defaults beyond what's in the schema.

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 this tool manages a queue of benchmark requests, with actions to read or stake. It distinguishes itself from siblings by focusing on measurement requests, though it doesn't name specific alternatives.

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 explains when to use each action: 'list' for reading, 'ask' for staking, and mentions the conditions (needs token for staking, open for reading). It doesn't explicitly say when not to use it versus alternatives, 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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