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fair_value

Read-onlyIdempotent

Convert a quoted prediction-market price into the implied probability under the Wang transform and get the premium, so you can strip favourite-longshot bias before comparing with your own forecast.

Instructions

Convert a market price into the probability it implies under the Wang transform, and report the premium.

Solves p_mkt = Phi(Phi^-1(p) + lam) for p; a pure calculation with no network
calls. Use it to strip the favourite-longshot premium from a quoted price
before comparing it with your own forecast. The default lam = 0.183 is the
pooled estimate in Yang (2026), SSRN 6468338; it pools real-money and
play-money venues, so treat it as illustrative. Returns market_price,
lambda, implied_probability and premium (price minus implied probability).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lam`	No	Pricing-wedge parameter lambda of the Wang transform. Positive values mean prices sit above the true probability, most of all for longshots. Default 0.183.
`market_price`	Yes	Observed YES price in dollars, strictly between 0 and 1.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.2.2Sep 30, 2026

addedInput schema / properties / lam / description

Added value: +"Pricing-wedge parameter lambda of the Wang transform. Positive values mean prices sit above the true probability, most of all for longshots. Default 0.183."

addedInput schema / properties / market_price / description

Added value: +"Observed YES price in dollars, strictly between 0 and 1."

First observedv1.2.1Sep 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent and non-open-world, so the safety profile is covered. The description goes beyond them by disclosing that it is a pure computation with no network calls, that the default lambda is a pooled/illustrative estimate with a cited source, and what the return payload contains.

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-loaded with the core purpose, then the formula, then usage, then the caveat and return fields. The formula and provenance sentence are justified, though the description runs a touch long for a two-parameter pure calculation.

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 stateless two-parameter calculation with an output schema present, the description covers purpose, usage context, the key parameter's provenance caveat, and the returned fields. Nothing an agent needs to invoke 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 baseline would be 3. The description adds provenance and interpretation for lam (pooled real-money/play-money estimate from Yang 2026, to be treated as illustrative) that the schema does not carry, and frames market_price as the quoted price being de-biased.

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 specific verb and transformation: 'Convert a market price into the probability it implies under the Wang transform,' plus the accompanying premium. The math (solving p_mkt = Phi(Phi^-1(p) + lam)) and the pure-calculation framing distinguish it immediately from live-data siblings like get_quote and get_orderbook.

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?

Gives clear when-to-use guidance: strip the favourite-longshot premium from a quoted price before comparing it with your own forecast. It stops short of explicitly naming alternative tools or stating when not to use it, so it is strong context rather than a full routing rule.

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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