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Evaluation dataset check

wever_evaluation-dataset-check
Read-only

Agent Evaluation: submit one evaluation dataset description: its name, size, source (production logs, written by experts, generated, a public benchmark or a mix), version and when it was published, the categories the agent must handle beside the categories covered with their example counts and whether hard and edge cases are included on purpose, how the reference labels were made (experts, trained annotators, crowd workers, a model, the agent’s own builders or no one) with the number of labelers, their agreement, a written guide and review of model labels, and the leakage controls: whether the set is held out from training and tuning, whether overlap was checked and found, whether prompt examples come from the set, whether anything was tuned against it, and the model’s training cutoff. The signed report says category by category whether the set covers what the agent must handle, whether the labels can be trusted and whether anything the agent learned from is in the test, and names every required category with no examples, no labels, examples shared with training or tuning data, prompt examples drawn from the set and tuning against the set, then every category under 30 examples, no hard cases, category counts above the dataset size, unreviewed model labels, labels by the agent’s builders, one labeler, agreement under 0.6, no guide, no split, no overlap check and a public benchmark published before the model’s training cutoff, with the fix for each. This reads the set an agent is scored on: whether a benchmark claim’s figures match the source it cites is wever_benchmark-evidence-check and whether a claim is supported by offered evidence is wever_claim-evidence-check. Verdict dataset_unfit, dataset_gaps, dataset_unverified or dataset_sound. POST /api/evaluation-dataset-check. Reads only the material in the call: no network request, fetch, money movement, message, sharing, configuration change or external write. Paid URL: 0.10 USDC per call on Base via x402 exact, with the shared 10 signed-wallet free calls per rolling 30 days. Bind X-Wever-Wallet-Proof to the exact JSON arguments and the tool route. Returns a report with an Ed25519 signed receipt. Contract: /api/agent-evaluation-contract. Human page: https://weverlabs.com/agent-evaluation/.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset": "<dataset>"
    },
    "name": "wever_evaluation-dataset-check"
  }
}

Input Schema

NameRequiredDescriptionDefault
labelNoOptional name recorded in the report.
datasetYes
leakageNo
coverageNo
labelingNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, not open-world, not destructive) it discloses that it reads only the material in the call with no network request, fetch, money movement, message, sharing, configuration change or external write, which is exactly the guarantee the openWorldHint=false hint needs. It also adds cost and auth behavior the annotations cannot express: 0.10 USDC per call on Base via x402 exact, 10 free signed-wallet calls per rolling 30 days, X-Wever-Wallet-Proof bound to the exact JSON arguments and tool route, and an Ed25519 signed receipt.

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?

Purpose is front-loaded correctly, but the body is one unbroken run-on paragraph in which the input list and the report list restate the same items (labeling, leakage, coverage) with near-identical enumerations. It also re-spells enum values already present in the schema (source, method, split), spending words on data the agent already has. Bullets or a short input/report split would carry the same content in far less text.

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

Completeness5/5

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

For a complex tool with nested objects and no output schema, the description covers everything needed to call it correctly: the endpoint (POST /api/evaluation-dataset-check), payment and wallet-proof mechanics, the permitted verdicts, the shape of the signed report, and the contract URL for deeper field semantics. Nothing material is left for the agent to guess.

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?

Although measured schema coverage is only 20%, the description walks through nearly every nested field: name, size, source options (production logs, expert-written, generated, public benchmark, mix), version and publication date, covered categories with example counts, required categories, deliberate hard/edge cases, labeling method with its enum values, labeler count, agreement, guide and model-label review, and all the leakage controls including split, overlap check, prompt-example reuse, tuning and training cutoff. This supplies operational meaning — what each field is used to judge and the threshold at which it fails (under 30 examples, agreement under 0.6) — that the bare schema does not.

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 names a specific verb and resource ('submit one evaluation dataset description' and it produces a verdict of dataset_unfit, dataset_gaps, dataset_unverified or dataset_sound), then adds the scoping sentence 'This reads the set an agent is scored on.' It also explicitly separates itself from wever_benchmark-evidence-check and wever_claim-evidence-check, so an agent can route between them without opening any schema.

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 an explicit routing rule: benchmark-claim-vs-source is wever_benchmark-evidence-check and claim-vs-offered-evidence is wever_claim-evidence-check, leaving this tool for dataset fitness. What it lacks is a negative condition — it never says when a dataset check is unnecessary (for example, when only the result metrics are in question, which is presumably wever_evaluation-result-check).

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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