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get_data_freshness

Read-only

Report the latest available data date per data domain (search, analytics, market share, AI visibility, ads, page experience) from live probes, with each domain's typical load cadence and a load-health flag. health is current within the expected cadence, delayed when slightly past it, and stale when the feed appears stalled — treat stale as a broken feed, not normal lag.

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": "get_data_freshness"
  }
}

Input Schema

NameRequiredDescriptionDefault
domainsNoSubset of domains; default all

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), so the bar is lower, and the description adds real context beyond them: 'from live probes' discloses that this is an active check, and the health/delayed/stale interpretation — especially 'treat stale as a broken feed, not normal lag' — is actionable behavioral guidance that prevents misevaluation.

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?

Front-loaded with the core purpose, followed by the health-flag semantics. Two tight sentences, no filler. The em-dash clause is dense but each part earns its place.

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

Completeness4/5

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

For a single-parameter read tool with no output schema, the description carries the full burden and does well: it covers what is reported, which domains apply, and how to read the health flag. The one omission is guidance on when to call it relative to other analysis tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and there are 0 enums, but the schema text ('Subset of domains; default all') never lists valid values. The description compensates by enumerating the six domains, effectively supplying the missing enum values, which is a genuine value-add over the schema.

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 and resource ('Report the latest available data date per data domain') and enumerates the exact domains covered (search, analytics, market share, AI visibility, ads, page experience). No sibling tool covers data freshness, so an agent can identify this immediately as the pre-flight freshness check.

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 — an agent infers this is a freshness/pre-flight check — but the description never states when to reach for this tool or what to do with the result relative to downstream analysis tools. No alternatives or exclusions are named. Adequate but with a clear gap.

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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