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Glama

DataPulse MY

by io.github.r3dz4r

Verify Dataset Before Trust

verify_dataset
Read-onlyIdempotent

Verify one dataset before trust in a single read-only call. Returns dataset metadata, the published health and evidence rows, and a fail-closed Sigstore per-dataset receipt verification result with artifact references. Use verify_dataset('fuelprice') before relying on a dataset claim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesCanonical dataset identifier to verify before trust, e.g. 'fuelprice'.
include_proof_stepsNoInclude bounded Cosign verifier output for audit steps, e.g. false.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds genuine behavioral context beyond annotations: it is fail-closed, returns specific bundled results, and includes artifact references. It does not fully explain failure behavior, but 'fail-closed' conveys the key trait.

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?

Three sentences with no filler. The core purpose is front-loaded, the return substance is summarized compactly, and the usage example earns its place.

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?

With an output schema present and rich annotations, the description covers purpose, usage timing, result contents, fail-closed behavior, and a concrete example. Nothing essential is missing for an agent to select and invoke the tool correctly.

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 parameters are already well documented. The description adds an example value 'fuelprice' but does not meaningfully expand on include_proof_steps or parameter semantics beyond what the schema provides.

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 starts with a specific verb and resource: 'Verify one dataset before trust in a single read-only call.' It clearly distinguishes this from sibling tools by targeting a single dataset and bundling metadata, health, evidence rows, and a Sigstore receipt verification result.

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 explicitly tells the agent when to call it: 'Use verify_dataset('fuelprice') before relying on a dataset claim.' It provides a concrete context and example, though it does not explicitly name alternatives or state when not to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DataPulse MY toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

Each tool targets a distinct workflow—search, detail retrieval, freshness checking, licence enumeration, and citation metadata. The only mild overlap is between get_dataset and get_provenance, both exposing metadata, but their descriptions differentiate full health/freshness detail from citation-ready provenance.

Naming Consistency5/5

All tool names follow a clear verb_noun pattern in snake_case: find_, get_, and search_ prefixes are used consistently. Minor stylistic variation between find_by_licence and find_stale does not undermine predictability.

Tool Count5/5

Five tools is well-scoped for a dataset catalog server, covering discovery, inspection, health assessment, licence scoping, and citation. No redundant or excessive tools are present.

Completeness4/5

The core lifecycle is covered: search to find datasets, get_dataset for full detail, find_stale for freshness risk, and get_provenance for citation. A minor gap is the lack of a general list-all or status filter beyond stale, but the domain is narrow enough to work around this.

Resources