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DataPulse

by io.github.r3dz4r
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Summarize Catalogue Freshness

get_freshness_summary
Read-onlyIdempotent

Return a freshness-at-a-glance summary of the published catalogue: fresh, aging, stale, and reference counts plus the latest health check time. Use it for catalogue-level freshness context; do not use it to enumerate affected datasets—use find_stale instead. It reads the latest published snapshot, so missing counts or check time mean the artifact omitted them; 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": "get_freshness_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive. The description goes beyond this by disclosing that it reads the latest published snapshot (and what missing counts mean), that DataPulse requires no API key, and that the edge imposes a rate limit of roughly one request per second with a small burst. This adds meaningful behavioral context that annotations alone do not convey.

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?

Two dense, well-organized sentences. The first sentence front-loads the core function and outputs; the second covers usage, limitations, and operational guidance. Every clause earns its place—no fluff or repetition.

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 output schema exists (so return structure doesn't need detailing), the description still summarizes key return contents, specifies when to use it, clarifies data source and completeness implications, and includes rate-limit guidance. Nothing an agent needs to call it correctly is missing.

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?

The tool has zero parameters, so the baseline is 4 per the rubric. The description does not include any parameter-specific details, which is appropriate since there are none to document. It correctly avoids fabricating meaning for a schema with no properties.

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 opens with a precise verb ('Return') and resource ('freshness-at-a-glance summary of the published catalogue'), lists the exact outputs (fresh, aging, stale, and reference counts plus latest health check time), and immediately distinguishes it from a sibling ('do not use it to enumerate affected datasets—use find_stale instead'). This is unambiguous and self-sufficient.

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 states when to use ('catalogue-level freshness context') and when not to ('enumerate affected datasets'), names the alternative tool (find_stale), and adds practical constraints (reads latest snapshot, rate limiting with pace/retry advice). The agent gains clear decision rules without needing to infer anything.

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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