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Machinepulse

Data Quality

data_quality

Report collector health — coverage, per-source freshness and missed snapshot runs with reasons, separating recent misses from the whole recorded history; internal-only sources are excluded. Use when the user asks whether the observatory data is fresh or complete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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?

With no annotations, the description carries the full burden, and it does disclose real behavioral traits beyond the schema: internal-only sources are silently excluded from results, and recent misses are reported separately from the entire recorded history. It does not state the freshness window computation or confirm the read-only nature, but for a zero-parameter report tool the disclosure is substantial.

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 sentences, both front-loaded: the first enumerates what is returned and what is excluded, the second gives the invocation trigger. No filler, no restatement of the title.

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?

An output schema exists, so the description is not obliged to explain return values, and with no parameters there is no argument contract to document. What remains missing is minor: whether the call is read-only/safe and how the freshness threshold is defined.

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 takes zero parameters, so the baseline is 4; there is no argument syntax for the description to clarify. The description instead correctly spends its words on result scoping, which is the only semantic risk here.

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?

States a specific verb and resource ('Report collector health') and enumerates the payload — coverage, per-source freshness, missed snapshot runs with reasons — plus a scope boundary ('internal-only sources are excluded'). It does not explicitly contrast itself with any sibling, but the resource is unmistakably distinct from the get_*_summary/leaderboard family.

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

Usage Guidelines4/5

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

'Use when the user asks whether the observatory data is fresh or complete' gives a concrete trigger condition, which is exactly the routing signal an agent needs. It stops short of naming alternatives or stating when not to use it (e.g. for per-source drill-down vs. this health roll-up).

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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