tradefloor

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Explain a price move by factor

explain_price_move

Read-onlyIdempotent

Decompose a day's simulated price move into 11 factor contributions to the mispricing, revealing which factors shifted prices; omit the ticker for largest moves.

Instructions

Break one day's move for each name into the 11 factor contributions that sum to the day's change in the mispricing, the log gap between the model price and fair value. Use it to ask which factors moved prices; use explain to trace one name's move down to the random draws behind it. They are the simulator's own bookkeeping, and they are not the whole price move. On the default preset most of the day's news and noise moves fair value, fair_value_shift takes that part out of the mispricing, and the fair-value move itself is not split up. Without a ticker it returns the top_n largest moves. Builds and runs its own market for up to 60 days; read-only.

Input Schema

TableJSON Schema

Name	Required	Description
`day`	No	The trading day to explain, 1 to 60.
`seed`	No	Simulation seed, an integer from 0 to 2**64 - 1. The same seed and arguments give the same result.
`top_n`	No	How many instruments to return, largest moves first, when no ticker is given. 1 or more.
`ticker`	No	One ticker from the roster. Omit to take the largest moves (explain_price_move) or the first name (explain).
`universe`	No	A roster document, usually the `universe` field of a build_universe result. Either {"size": n, "seed": s, "sectors": [...]} or {"instruments": [...]}. When given it replaces universe_size, universe_seed and universe_sectors.
`universe_seed`	No	Seed that generates the roster, separate from the simulation seed. Ignored when `universe` is given.
`universe_size`	No	Names in a generated roster, 2 to 120. Ignored when `universe` is given.
`universe_sectors`	No	Lowercase sector ids to concentrate a generated roster on, for example ["technology", "energy"]. The ids: technology, financial_services, healthcare, energy, consumer_discretionary, consumer_staples, industrials, materials, real_estate, utilities, telecommunications, transportation. A concentrated roster is a named envelope gap, and the result says so.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 4, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world. The description adds genuinely useful context beyond them: the factors are 'the simulator's own bookkeeping,' they are 'not the whole price move,' and on the default preset fair_value_shift removes news/noise from the mispricing. It also notes the 60-day market build. Return format is left to the output schema.

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?

Front-loads the core purpose in the first sentence, then layers caveats and sibling routing. Dense and mostly earns its length, though the fair-value/mispricing explanation is slightly winding and could be tightened.

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 an 8-parameter factor-decomposition tool with an output schema, the description covers the interpretation caveats, sibling routing, no-ticker behavior, and read-only/self-contained nature. Nothing an agent needs to call it correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the ticker/top_n interplay ('Without a ticker it returns the top_n largest moves') and the fair-value decomposition caveat that shapes interpretation of the returned factors.

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?

States a precise verb and resource: break one day's move into 11 factor contributions that sum to the change in the mispricing. It also names the sibling it is not (explain) and clarifies the scope (mispricing, log gap between model price and fair value). An agent can distinguish it from explain without opening either schema.

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

Usage Guidelines5/5

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

Explicitly routes: 'use it to ask which factors moved prices; use explain to trace one name's move down to the random draws behind it.' It also documents the no-ticker behavior (returns top_n largest moves) and the fair_value_shift caveat, giving clear selection conditions.

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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