jawz

Get weekly data releases

get_weekly_data_releases

Read-only

Past week's data releases with H.4.1 net liquidity update. Anchored to Friday — most current when run Friday morning or later. Each release classifies actual vs consensus where BOTH are on file (both Mako-curated): surprise is hot / modestly hot / in-line / modestly cool / cool on the headline metric, vs_consensus is the same comparison stated neutrally as above/below/in-line, and metrics breaks it down per metric. hot/cool is DIRECTIONAL versus consensus, not a verdict — a hot CPI and a hot payrolls print mean opposite things for the same book. surprise: "n/a" means the comparison could not be made; implication names which half is missing. consensus_provenance.age_days is measured at week_ending, not at call time, so re-asking for an earlier week returns the same age it did the first time.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`week_ending`	No	Week ending date (YYYY-MM-DD); defaults to last Friday

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`week_ending`	No
`data_sources`	No
`data_releases`	No
`policy_events`	No
`fed_h41_update`	No
`terms_glossary`	No
`staleness_flags`	No

TDQS

A4.4/5.0

Behavior5/5

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

The description goes well beyond the readOnly/destructive annotations by explaining how classifications are derived, what 'surprise' and 'vs_consensus' mean, the directional rather than verdict-like nature of hot/cool labels, the meaning of 'n/a', and the stable age_days semantics across re-requests. This is rich, non-obvious behavioral context.

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?

The description is dense but every sentence serves a purpose, covering output semantics, timing, and edge cases. It is front-loaded with the core purpose and Friday anchoring, though the long single paragraph could benefit from slight structural separation.

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, read-only annotations, and an output schema present, the description fully covers the nontrivial behavioral nuances: consensus provenance, missing-comparison cases, and directional interpretation. Nothing essential for correct invocation or interpretation 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?

The single parameter is already fully documented in the schema (week_ending with date format and default to last Friday). The description reinforces the Friday anchoring and clarifies that consensus age is measured at week_ending, but it does not need to add much because schema coverage is 100%.

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 clearly identifies the tool as returning the past week's data releases with an H.4.1 net liquidity update. It distinguishes this from sibling data tools by specifying the weekly cadence, Friday anchoring, and the consensus-vs-actual classification structure.

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?

It gives concrete timing guidance: the data is anchored to Friday and most current when run Friday morning or later. It does not explicitly name alternatives or state when not to use this tool, so it stops just short of full exclusion guidance.

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.

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