jawz

Run mode

run_mode

Returns the published framework text for one named mode within a chapter, rather than the full chapter. Repeat calls return the same mode content — the text is not regenerated per call. Each call appends one usage-counter row and returns a new invocation_id used for feedback.

Input Schema

TableJSON Schema

Name	Required	Description
`node`	Yes	Chapter node
`context`	No	Optional user context
`loop_slug`	Yes	Loop slug
`mode_slug`	Yes	Mode slug (e.g. 'equity-screen', 'regime-overlay')

Output Schema

TableJSON Schema

Name	Required	Description	Default
`node`	No
`loop_slug`	No
`mode_slug`	No
`loop_title`	No
`author_name`	No
`content_note`	No
`invocation_id`	No
`terms_glossary`	No
`framework_content`	No

TDQS

A4.5/5.0

Behavior5/5

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

The description discloses important behavioral traits beyond the annotations: calls are idempotent in content ('text is not regenerated per call'), each call has a side effect ('appends one usage-counter row'), and a new invocation_id is returned for feedback. This is useful context beyond readOnlyHint=false and destructiveHint=false.

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 with zero waste: core purpose, idempotent content behavior, and side-effect/feedack behavior. Each sentence earns its place and the most important disambiguating information is front-loaded.

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 an output schema and annotations, the description adds exactly the missing behavioral context: non-read-only behavior, idempotency, usage counting, and invocation_id for feedback. An agent can safely select and invoke this tool knowing what will happen and what to expect.

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 coverage is 100%, so the schema already documents all parameters with descriptions. The description does not add extra parameter-level meaning beyond generic framing like 'named mode' and 'chapter', matching the schema's loop_slug, node, and mode_slug. Baseline of 3 is appropriate.

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 ('Returns') and resource ('published framework text for one named mode within a chapter'), and contrasts it with the full-chapter behavior, distinguishing it from sibling tools like get_chapter and run_chapter. This gives an agent a clear, actionable definition.

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 description clearly implies when to use the tool: when only one named mode's framework text is needed, not the full chapter. It also provides a when-not signal ('rather than the full chapter'), though it does not explicitly name the full-chapter alternative tool.

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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