DataPulse

Identify Freshness and Schema Risks

find_stale

Read-onlyIdempotent

Return datasets whose status is aging, stale, or degraded, plus datasets missing from the latest health snapshot. Use when an agent needs to know which data has a freshness or schema-validity risk. Use it to enumerate freshness or schema-risk candidates; do not use it for anomalies, trends, reliability, or drift—use find_anomalies, find_deteriorating, find_unreliable, or find_schema_drift instead. It reads the published health snapshot, so an empty result means no rows met this snapshot-based rule; 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_stale"
  }
}

Input Schema

Name	Required	Description	Default
`max_age_hours`	No	Maximum published-snapshot age before otherwise healthy rows are included, e.g. 72; omit it to use 24 hours.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 21, 2026

changedInput schema / properties / max_age_hours / description

Previous value: -"Maximum acceptable age of the latest health check in whole hours; non-negative integer, e.g. 72."New value: +"Maximum published-snapshot age before otherwise healthy rows are included, e.g. 72; omit it to use 24 hours."

Changed1 schema field changedAug 13, 2026
- addedInput schema / properties / max_age_hours / examples
```
Added value: +[
+  24,
+  72
+]
```

Changed2 schema fields changedAug 8, 2026

addedInput schema / properties / max_age_hours / description

Added value: +"Maximum acceptable age of the latest health check in whole hours; non-negative integer, e.g. 72."

addedInput schema / required
```
Added value: +[]
```

First observedAug 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations, the description adds meaningful behavioral context: it reads the published health snapshot, empty results mean no rows met the snapshot-based rule, DataPulse is read-only, no API key is required, and there is a one-request-per-second edge limit with a small burst. These details help an agent set expectations and handle retries.

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 dense but every sentence earns its place: the first defines the function, the second and third cover usage and exclusions, and the fourth adds essential behavioral and rate-limit context. It is front-loaded with the core purpose before routing to alternatives.

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 tool with one optional parameter, rich annotations, an output schema, and explicit sibling distinctions, this description is complete. It covers what the tool returns, when to use it, what not to use it for, empty-result semantics, authentication, and rate-limit behavior—nothing essential is missing.

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 fully documents max_age_hours with examples and defaults. The main description does not add extra meaning about the parameter beyond what the input schema already provides, which aligns with the baseline of 3 for high schema coverage.

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 status is aging, stale, or degraded, plus datasets missing from the latest health snapshot.' It clearly distinguishes this tool from siblings by naming the relevant risk categories and by referencing the published health snapshot, which anchors its scope.

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 gives explicit when-to-use guidance: use when an agent needs to know freshness or schema-validity risk and to enumerate candidates. It also gives strong negative guidance: do not use for anomalies, trends, reliability, or drift, and explicitly names find_anomalies, find_deteriorating, find_unreliable, and find_schema_drift as alternatives.

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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