Dataddo Data to AI

Refresh check

refresh

Read-only

Check whether refreshing an entity would return fresher data. It changes nothing: flow-backed answers are already refetched whenever the platform's version of the flow moves, and this server never makes the platform re-extract from the source system.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity": "<entity>"
    },
    "name": "refresh"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	model name; omit for the default
`entity`	Yes	entity to explain freshness for

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 10, 2026

changedInput schema / properties / entity / description

Previous value: -"entity whose upstream data should be refreshed"New value: +"entity to explain freshness for"

Changed1 schema field changedOct 7, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
First observedOct 6, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive, but the description adds real behavioral context beyond them: flow-backed answers are already refetched when the platform's flow version moves, and the server never triggers re-extraction from the source system. This tells the agent what a freshness check can and cannot accomplish, which is genuinely useful for decision-making.

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?

Two dense sentences, front-loaded with the purpose and the 'changes nothing' caveat. No filler, though the second sentence is slightly convoluted in phrasing ('whenever the platform's version of the flow moves').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The core is covered: no side effects, scope of freshness judged, and no output schema exists so return values need not be spelled out. However, an agent is left guessing what a 'check' actually produces (a boolean, a staleness verdict, an explanation), which matters for a tool whose whole purpose is a diagnostic answer.

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%, so both parameters are already documented in the schema, including the nullable model default. The description adds no meaning about the 'model' parameter or how entity identifiers are formed, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: it checks whether refreshing an entity would yield fresher data, and explicitly corrects the potentially misleading name 'refresh' by stating it changes nothing. It does not distinguish itself from siblings such as data_status or describe_entity, so an agent must infer the boundary itself.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: an agent can infer it should call this before attempting a refresh to see whether one is worthwhile. There is no explicit 'use when / do not use when' framing and no alternative sibling is named for overlapping questions like data_status.

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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