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tresor4k

macalc

calculate_expected_value_bet

Calculate expected value and profitability of a bet using win probability, win amount, loss amount, and optional bet cost to determine expected return and loss probability.

Instructions

Calculate expected value and profitability of a bet or investment decision. Returns: {lose_probability}. See list_bundles for related 'jeux-probabilites' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
win_probabilityYesProbability of winning (0 to 1)
win_amountYesNet amount won if outcome is positive
loss_amountYesNet amount lost if outcome is negative
bet_costNoUpfront cost to enter the bet (default 0)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoComputed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.
Behavior2/5

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

No annotations provided, so the description must fully convey behavior. It only states the calculation and a return field. It does not disclose whether the operation is read-only, if any side effects exist, or any prerequisites. The formula is implied but not explicitly stated.

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 concise sentences with no wasted words. The first sentence states the primary action, and the second provides a specific return field and a pointer to related tools. Well-structured for minimal length.

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

Completeness3/5

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

Given 100% schema coverage and an output schema (not shown), the description is adequate but not thorough. It explains the basic purpose and one return value, but does not elaborate on the formula or how to interpret results. For a simple calculator, this may be sufficient, but leaves room for ambiguity.

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 coverage is 100% with descriptions for all parameters. The description adds no additional parameter meaning beyond the schema. It mentions a return field ('lose_probability'), which is about output, not parameter semantics. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Calculate expected value and profitability of a bet or investment decision.' It specifies the verb and resource, and provides a hint about the return value. While it distinguishes from many siblings by focusing on expected value, it could be more precise about 'profitability' and how it differs from similar probability calculators.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The only hint is 'See list_bundles for related calculators', which directs to a bundle but does not clarify selection criteria. There is no when-to-use or when-not-to information.

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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