DataPulse

Identify Dataset Update Anomalies

find_anomalies

Read-onlyIdempotent

Return datasets flagged by the latest published anomaly detection (anomalies), ranked by how far the observed update interval exceeds its threshold. Optionally require a minimum publish-reliability grade; includes pipeline-computed anomaly and reliability evidence so agents do not recompute it. Use it for unusual update intervals; do not use it for worsening freshness, recovery, reliability grades, or structural drift—use find_deteriorating, find_recovering, find_unreliable, or find_schema_drift instead. It reads precomputed anomaly data, so an empty result means no published row survives the selected filters; 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

Name	Required	Description
`mode`	No	Optional exact published detection mode filter, e.g. 'rolling_14d' or 'cadence_fallback'; omit it to include every mode.
`limit`	No	Maximum highest-ranked anomalies to return, e.g. 50; omit it to use 50 without changing the ranking.
`min_reliability`	No	Optional inclusive published reliability floor, e.g. 'C' keeps A, B, and C; omit it to retain rows regardless of grade.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 21, 2026

changedInput schema / properties / limit / description

Previous value: -"Maximum ranked anomalies to return; integer from 1 to 200, e.g. 50."New value: +"Maximum highest-ranked anomalies to return, e.g. 50; omit it to use 50 without changing the ranking."

changedInput schema / properties / min_reliability / description

Previous value: -"Optional minimum publish-reliability grade; e.g. 'C' keeps A, B, and C and excludes insufficient data."New value: +"Optional inclusive published reliability floor, e.g. 'C' keeps A, B, and C; omit it to retain rows regardless of grade."

changedInput schema / properties / mode / description

Previous value: -"Optional exact detection mode; e.g. 'rolling_14d' or 'cadence_fallback'."New value: +"Optional exact published detection mode filter, e.g. 'rolling_14d' or 'cadence_fallback'; omit it to include every mode."

AddedAug 17, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already mark read-only, idempotent, and non-destructive, but the description adds new context: it reads precomputed data, empty result semantics, no API key requirement, and rate limiting ('roughly one request per second with a small burst'). This exceeds what annotations provide.

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?

Four sentences, each carrying distinct value: purpose, evidence inclusion, usage routing, and operational caveats. No repetition of schema fields or annotations. Front-loaded with the core action and ranking, then conditional and exclusion guidance.

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?

Complete for a read-only query tool with an output schema. It covers purpose, alternatives, empty-result handling, authentication, and rate limits. The output schema already handles return value details, so no further description is needed.

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 baseline is 3. The description adds nominal context ('minimum publish-reliability grade' maps to min_reliability; 'ranked' relates to limit) but does not materially exceed the schema's parameter descriptions. No compensation needed.

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+resource ('Return datasets flagged by the latest published anomaly detection') and the ranking criterion ('how far the observed update interval exceeds its threshold'). Explicitly distinguishes from sibling tools by naming what it is not for and pointing to find_deteriorating, find_recovering, find_unreliable, and find_schema_drift.

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?

Gives explicit when-to-use ('Use it for unusual update intervals') and when-not-to-use ('do not use it for worsening freshness, recovery, reliability grades, or structural drift') with named alternatives. This leaves no ambiguity about tool selection.

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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