DataPulse

Identify Deteriorating Dataset Trends

find_deteriorating

Read-onlyIdempotent

Return datasets whose published freshness trend is deteriorating, ranked by staleness slope. Optionally require a minimum historical anomaly rate; includes pipeline-computed trend and reliability evidence so agents do not recompute it. Use it for worsening freshness trends; do not use it for anomalies, recovery, reliability grades, or structural drift—use find_anomalies, find_recovering, find_unreliable, or find_schema_drift instead. It reads precomputed trend data, so an empty result means no published deteriorating 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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_deteriorating"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum highest-ranked deteriorating datasets to return, e.g. 50; omit it to use 50 without changing ranking.
`min_anomaly_rate`	No	Optional inclusive anomaly-rate filter on published history, e.g. 25.0; omit it to retain every deteriorating row.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 21, 2026

changedInput schema / properties / limit / description

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

changedInput schema / properties / min_anomaly_rate / description

Previous value: -"Optional minimum percent of anomaly-evaluable history days, e.g. 25.0."New value: +"Optional inclusive anomaly-rate filter on published history, e.g. 25.0; omit it to retain every deteriorating row."

AddedAug 17, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it reads precomputed trend data, an empty result means no deteriorating row survives the filters, DataPulse is read-only and requires no API key, and there is a rate limit of roughly one request per second with a small burst. This is useful operational context that annotations do not provide. It does not contradict 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 a single dense paragraph that front-loads the core purpose and ranking criterion, then covers exclusions, alternatives, and operational notes. It earns its length by packing in usage guidance and rate-limit context, though it could be slightly more scannable with sentence breaks. No wasted words.

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?

Given the tool has only 2 optional parameters, a rich output schema, and annotations covering read-only/idempotent behavior, the description is complete. It explains what the tool returns, how results are ranked, when to use it, when not to use it, what an empty result means, and the rate limit. An agent has everything needed to select and invoke it 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 the schema already documents both parameters thoroughly, including defaults, ranges, examples, and semantics. The description adds a small amount of context by mentioning 'minimum historical anomaly rate' and 'staleness slope' ranking, but it does not need to compensate for any schema gap. Baseline 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?

The description states a specific verb ('Return'), a specific resource ('datasets whose published freshness trend is deteriorating'), and a ranking criterion ('staleness slope'). It also explicitly distinguishes itself from four sibling tools by name, so an agent can tell it apart from find_anomalies, find_recovering, find_unreliable, and find_schema_drift without opening their 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?

The description explicitly says when to use it ('Use it for worsening freshness trends') and when not to use it ('do not use it for anomalies, recovery, reliability grades, or structural drift'), naming the exact alternative tools. It also clarifies that it reads precomputed trend data, so agents know not to recompute it. This is explicit when/when-not/alternatives guidance.

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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