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predictionmarketspicks

PredictionMarketsPicks Quant

Official

Base Rate Gap

base_rate_gap
Read-only

Compare a prediction market price to a historical base rate to identify mispricing. Get the percentage point gap, directional signal, and sample quality.

Instructions

Compare a market price against the historical base rate for a class of events and get the gap in percentage points plus a signal and sample-size quality. Pass either a known base-rate id (one of: incumbent_reelected, fed_hold_unemp_below_4, fed_cut_cpi_above_3, recession_called_12mo, sp500_positive_year, bitcoin_above_100k_eoy, gdp_growth_above_2, cpi_above_3, senate_incumbent_wins_primary, vix_below_20_eoy, interest_rate_cut_next_meeting, major_sports_upset) or your own baseRateValue. Use for "how does this price compare to history", "is the market ignoring the base rate", "historical frequency vs market".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseRateIdNoKnown base-rate id to look up (includes sample size + source).
marketPriceYesCurrent market price in cents / implied probability % (0–100).
baseRateValueNoYour own base rate in % (0–100), used when no baseRateId is given.
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds that it returns gap, signal, and sample-size quality, providing useful output context beyond the annotation.

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?

The description is two sentences, front-loaded with the main function and a list of example use cases, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains the output types (gap, signal, sample-size quality). It could detail the return format, but the core functionality is well-covered.

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 well-documented. The description reinforces the options but doesn't add significant new meaning beyond the schema.

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 states the tool compares a market price against a historical base rate, outputs gap, signal, and sample-size quality. It distinguishes itself from sibling tools like bayes_update, calculate_ev, and kelly_size by focusing on base rate comparison.

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?

Provides explicit use case examples and explains when to pass baseRateId vs baseRateValue. While it doesn't explicitly state when not to use, the examples and alternative input paths give clear context.

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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