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Overfitting odds

overfitting_odds
Read-onlyIdempotent

Overfitting check: how many attempts plain chance needs to produce the track record you were shown. Give the wins, the losses and how many versions were tried: you get the exact binomial odds of one try reaching it, the odds once somebody shows you the best of N, and how many tries would make it an even bet. Arithmetic only — no market data, no model, no opinion. A 62% win rate over 100 trades is one thing on the first try, nothing on the fiftieth.

Use when you are shown a win rate, a backtest or a signal channel's record and need to know whether luck explains it. Needs no coin. For what a named rule did on real prices use strategy_grid_lookup; to place a return among real accounts, leaderboard_rank_check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
winsYesWinning trades.
triesNoHow many versions were tried before this one was shown to you. Defaults to 1, which is almost never true.
lossesNoLosing trades. Give this or trades.
tradesYesTotal trades.
baselineNoProbability a single trade wins by chance. Defaults to 0.5.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNofalse when we do not hold that data. Never a zero standing in for an answer.
errorNono_data when ok is false.
reasonNoWhy, in one sentence, and what we do have instead.
readingNoThe same three numbers in one sentence.
odds_one_tryNoP(at least this many wins) for a single attempt. Exact binomial, not simulated.
win_rate_pctNoThe win rate.
odds_best_of_triesNoSame result, once you take the best of the attempts made.
tries_for_even_oddsNoAttempts needed for chance alone to reach it half the time.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / baseline / default
      Added value: +0.5
    • addedInput schema / properties / tries / default
      Added value: +1
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond them: 'Arithmetic only — no market data, no model, no opinion' tells the agent this is a pure deterministic calculation needing no external inputs, and it notes no coin is required.

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?

The operational content is front-loaded and the second paragraph cleanly separates the when-to-use rule from the sibling routing. It is a touch prose-heavy, but every clause (pure arithmetic, no coin, the 62% illustration) carries information.

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 pure-arithmetic tool with an output schema, full-annotation coverage and complete schema descriptions, the definition supplies everything needed: trigger, routing, input expectations and the deterministic nature of the computation.

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 is 3, but the description reinforces the semantics of 'tries' with the worked example ('62% over 100 trades is one thing on the first try, nothing on the fiftieth'), clarifying that the parameter dominates the result.

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 and resource: computing the binomial odds that chance alone explains a shown track record from wins, losses and tries. It explicitly distinguishes itself from siblings by naming strategy_grid_lookup and leaderboard_rank_check as the tools for different questions.

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?

It gives an explicit trigger ('Use when you are shown a win rate, a backtest or a signal channel's record and need to know whether luck explains it') and routes the agent elsewhere for the two adjacent cases by naming the alternatives.

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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