jawz

Get data health

get_data_health

Read-only

Freshness status for every data source Jawz reads: per-source release date, latest observation date, expected cadence, age in days, and whether any source is stale or unavailable. Also reports whether the ingestion pipeline is keeping up, and carries plain-language notices when a scheduled update has not arrived. Applies when the reliability of a figure depends on how current its underlying data is.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`notices`	No
`sources`	No
`any_stale`	No
`collector`	No
`terms_glossary`	No
`any_unavailable`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context beyond that: it reports stale/unavailable sources, ingestion pipeline health, and plain-language notices about missed updates. No contradiction with annotations.

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?

Three sentences, each earning its place: output overview, additional health signals, and usage context. There is no redundant or filler text.

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?

With zero parameters, read-only annotations, and an output schema to define the return structure, the description fully covers what an agent needs: what is returned, when the tool is appropriate, and how it differs from other data-access 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?

The tool has zero parameters, making schema description coverage trivially 100% and establishing a baseline of 4. No parameter descriptions are needed because the tool takes no input.

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 names the resource ('data health') and specifies the exact output: per-source release date, latest observation date, expected cadence, age in days, staleness flags, ingestion pipeline status, and plain-language notices. This clearly differentiates it from siblings like get_prices or get_event_calendar.

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?

The final sentence provides a clear usage condition: use when the reliability of a figure depends on how current its underlying data is. It does not explicitly name alternatives or when-not-to-use cases, but the context is unambiguous.

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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TDQS

A4.1/5.0

Disambiguation4/5

Most tools target clearly distinct resources—regime, liquidity, conditions, prices, ETF profiles, data health—and the three history tools are explicitly separated as price, flow, and judgment. The main ambiguity is get_chapter vs run_chapter, which both return chapter framework content and differ only in usage logging, though the descriptions call this out explicitly.

Naming Consistency4/5

The set follows a consistent snake_case verb_noun pattern: get_ for reads, list_ for enumeration, run_ for framework text, and score_ for position drift. The only wrinkle is run_chapter/get_chapter, where 'run' doesn't mean execution but rather 'return framework text and log usage,' making the verb semantics slightly less predictable.

Tool Count4/5

22 tools is on the heavy side but the set is organized into recognizable clusters: macro regime, liquidity/conditions, histories, portfolio drift, ETF/prices, loops/framework, and data health. Each tool appears to earn its place, so the count is slightly high but not bloated.

Completeness4/5

The surface is comprehensive for a read-and-analyze macro/portfolio server: current reads, historical timeseries, data freshness, event calendar, ETF look-through, drift scoring, and loop navigation are all covered. Minor gaps exist—no direct portfolio/position listing tool and non-US central-bank event dates are intentionally not tracked—but these are acknowledged and workable.

Resources

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