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Verify Dataset Before Relying

verify_dataset
Read-onlyIdempotent

This is the preferred single-call pre-trust check for 'is this dataset current?', stale, unknown-freshness, degraded, or browser-dependent questions, and whenever an agent must verify before relying on data. Returns dataset metadata, published evidence and fail-closed signed receipt verification with artifact references. It verifies published artifacts, not a live source check: you may infer whether their receipt verifies, but must not infer current upstream availability or semantic truth. Use search_datasets → verify_dataset → get_provenance. Use it for one published pre-trust check; do not use it for a live transport comparison or attestation-chain verification—use verify_evidence or verify_attestation instead. It verifies precomputed published artifacts, so a failed check does not identify current upstream availability; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesStable dataset slug returned by search_datasets for the published receipt check, e.g. 'fuelprice'; this tool requires the slug, not a display name.
include_proof_stepsNoSet true to include bounded verifier diagnostics for an audit, e.g. false; omit it to suppress diagnostics without changing verification.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / dataset_id / description
      Previous value: -"Canonical dataset identifier for the published pre-trust receipt check, e.g. 'fuelprice'; this does not perform a live source fetch."New value: +"Stable dataset slug returned by search_datasets for the published receipt check, e.g. 'fuelprice'; this tool requires the slug, not a display name."
    • changedInput schema / properties / include_proof_steps / description
      Previous value: -"Include bounded signed-receipt verifier diagnostics for an audit, e.g. false; the result still does not establish upstream semantic truth."New value: +"Set true to include bounded verifier diagnostics for an audit, e.g. false; omit it to suppress diagnostics without changing verification."
  2. Changed2 schema fields changed
    • changedInput schema / properties / dataset_id / description
      Previous value: -"Canonical dataset identifier to verify before trust, e.g. 'fuelprice'."New value: +"Canonical dataset identifier for the published pre-trust receipt check, e.g. 'fuelprice'; this does not perform a live source fetch."
    • changedInput schema / properties / include_proof_steps / description
      Previous value: -"Include bounded Cosign verifier output for audit steps, e.g. false."New value: +"Include bounded signed-receipt verifier diagnostics for an audit, e.g. false; the result still does not establish upstream semantic truth."
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and non-destructive hints. The description goes beyond them by disclosing that this verifies precomputed published artifacts rather than live sources, that a failed check does not indicate current upstream availability, that verification is fail-closed, and that semantic truth must not be inferred. No contradiction with 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 each sentence carries useful information: purpose, scope, limitations, pipeline, alternatives, and rate limits. There is minor redundancy around the 'verifies published artifacts, not a live source' point, but the front-loaded purpose and the clear structure keep it effective.

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 tool with this complexity and an existing output schema, the description covers all needed operational context: when to use, what it returns, what it cannot tell the agent, alternatives, pipeline ordering, read-only auth behavior, and rate limits. Nothing critical is missing.

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 100%, so the schema already fully documents both parameters, including the required slug semantics and the include_proof_steps behavior. The description does not meaningfully add parameter-level detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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?

States a specific verb (verify) and resource (dataset), plus a clear scope: a single-call pre-trust check for questions about currentness/staleness/freshness. It also distinguishes itself from the sibling verify_evidence and verify_attestation by naming what it is not, so an agent can route correctly without opening other schemas.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('pre-trust check', 'before relying on data'), when not to use it ('live transport comparison' or 'attestation-chain verification'), names the alternative tools, and even gives the intended pipeline order: search_datasets → verify_dataset → get_provenance. Rate-limit pacing guidance is also included.

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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