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datapulse-my

by r3dz4r
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Identify Recovering Dataset Trends

find_recovering
Read-onlyIdempotent

Identify datasets with improving freshness trends, ranked by fastest staleness reduction. Use to track recovery in publish reliability, backed by pipeline-computed trend evidence.

Instructions

Return datasets whose published freshness trend is recovering, with the fastest staleness reductions first. Includes pipeline-computed trend and publish-reliability evidence. Use it for improving freshness trends; do not use it for deterioration, anomalies, reliability grades, or structural drift—use find_deteriorating, find_anomalies, find_unreliable, or find_schema_drift instead. It reads precomputed trend data, so an empty result means no published recovering row exists; 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
limitNoMaximum fastest-recovering datasets to return, e.g. 50; omit it to use 50 without changing ranking.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.36.1
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum ranked recovering datasets to return; integer from 1 to 200, e.g. 50."New value: +"Maximum fastest-recovering datasets to return, e.g. 50; omit it to use 50 without changing ranking."
  2. Addedv1.0.2

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: it reads precomputed trend data, explains the meaning of an empty result, states that it requires no API key, and discloses the rate limit with pacing advice. All of this is useful and consistent with the readOnlyHint, idempotentHint, openWorldHint, and destructiveHint annotations.

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?

The description is compact and front-loaded: purpose first, then usage guidance, then behavioral/rate-limit details. Every sentence adds necessary information without redundancy or filler.

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 single-parameter read-only tool with an output schema, the description covers purpose, selection criteria, output semantics, authentication, and rate limits. Nothing an agent needs to invoke it correctly is missing from the textual description.

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 coverage is 100% and the single 'limit' parameter is already well-documented with defaults, bounds, and examples. The description adds a small clarification that omitting limit keeps ranking unchangedebb, but the parameter's core meaning is fully conveyed by the schema, so the baseline score applies.

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 and resource: 'Return datasets whose published freshness trend is recovering' with a clear ordering ('fastest staleness reductions first'). It also distinguishes itself by naming exact alternatives for other cases, so an agent can immediately tell it apart from sibling tools.

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?

It explicitly says when to use this tool ('Use it for improving freshness trends') and when not to use it, listing the specific sibling tools for deterioration, anomalies, reliability grades, and structural drift. This is the strongest possible guidance for 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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