jawz

Score position drift

score_position_drift

Read-only

Score how a single position's regime fit has drifted since entry. Returns regime_at_entry, regime_now, fit_score_at_entry, fit_score_now, drift_score (now - entry), drift_label (improved/stable/deteriorated/review_now), explanation, and review_questions. No buy/sell recommendation — output is observational. Supports Chapter 4 Mode 4.2 (Position Retrospective) for single-name regime-fit review and Mode 4.3 (Thesis Status Sweep) for per-position drift across the book.

Input Schema

TableJSON Schema

Name	Required	Description
`symbol`	Yes	Ticker or instrument (e.g. AAPL, BTC, TLT)
`thesis`	No	Original thesis text. Echoed back word-for-word in review questions.
`conviction`	No	User-supplied conviction 1–10. Drives conviction_gap if a meaningful gap exists vs the regime fit score.
`entry_date`	Yes	ISO date (YYYY-MM-DD) when the position was opened
`asset_class`	Yes	Asset class for fit-score lookup, drawn from the holding's actual exposure (Chapter 2 Mode 2.1) rather than from the current regime. A hybrid holding is scored per sleeve and weight-blended. Drives the regime-fit calculation.
`symbol_type`	No	Price-API hint. Inferred from asset_class if omitted.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	No
`price_now`	No
`as_of_date`	No
`entry_date`	No
`regime_now`	No
`asset_class`	No
`drift_label`	No
`drift_score`	No
`explanation`	No
`fit_score_now`	No
`conviction_gap`	No
`price_at_entry`	No
`terms_glossary`	No
`regime_at_entry`	No
`staleness_flags`	No
`review_questions`	No
`total_return_pct`	No
`fit_score_at_entry`	No
`classification_warnings`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already signal readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: output is observational, no buy/sell recommendation, and it returns drift labels and review questions. This goes beyond the annotations without contradicting them.

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, front-loaded with the action and scope, then behavior and supported modes. The output-field list is compact and useful despite an output schema existing. No wasted words.

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?

Given the rich input schema, output schema, and readOnly annotations, the description is complete enough for correct invocation. It covers purpose, output behavior, observational nature, and the modes it supports. No critical gap remains for an agent deciding whether and how to call it.

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 each parameter is already documented with type, enum, and meaning. The description reinforces that the tool is position-level and entry-date-based, but it does not add new parameter-level semantics beyond what the schema provides. Baseline 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?

Uses specific verb 'Score' with a clear resource: 'a single position's regime fit has drifted since entry.' It also enumerates output fields and distinguishes itself from siblings by stressing that it gives no buy/sell recommendation and by mapping to specific Chapter 4 Modes. This makes it easy to tell apart from tools like get_drift_alerts or run_mode.

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?

Dscribes when it applies (Mode 4.2 position retrospective and Mode 4.3 thesis status sweep) and sets expectation that output is observational with no recommendation. It does not explicitly name alternatives or state when not to use it, ut it provides enough contextual guidance for an agent to select it appropriately.

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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