Calcgrid

Odds Converter

convert_betting_odds

Read-onlyIdempotent

Use this when you have odds in one format and need the other two, the implied probability of a price, or the payout of a stake at those odds. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery.

Do not use this when you need the combined price of several selections (use parlay-odds), the value of a bet given your own probability estimate (use bet-expected-value), or a bookmaker's margin across a whole market (use arbitrage-bet). Mathematics only; not gambling advice. What it computes: Converts betting odds between American (moneyline), decimal (European) and fractional (UK) formats, auto-detecting the input format, and reports the implied probability, profit and total return for a stake. Inputs: odds (string); format (enum, optional); stake (number, optional). Complete JSON argument examples: {"odds":"+150"} | {"odds":"-200","format":"auto","stake":100} Outputs: american, decimal, fractional, implied_probability_percent [%], profit_on_stake, total_return, detected_format. Formula: decimal = 1 + american/100 (american > 0) or 1 + 100/|american| (american < 0); decimal = 1 + a/b (fractional a/b); american = +100·(decimal − 1) if decimal ≥ 2, else −100/(decimal − 1); fractional = (decimal − 1) reduced with the GCD; implied_probability_percent = 100/decimal; total_return = stake × decimal Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/odds-converter with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/gaming/odds-converter.md

Input Schema

TableJSON Schema

Name	Required	Description	Default
`odds`	Yes	Odds in any format: American +150 or -200, decimal 2.50, fractional 3/2 or 3-2, or evens.
`stake`	No	Stake used for the profit and return outputs (any currency).
`format`	No	Format of the odds input. Auto: a/b or a-b with digits on both sides is fractional, a sign or an integer of 100 or more is American, anything else is decimal.	auto

Output Schema

TableJSON Schema

Name	Required	Description	Default
`links`	No
`result`	Yes
`request`	Yes
`sources`	No
`success`	Yes
`version`	No
`freshness`	No
`timestamp`	Yes
`next_actions`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 26, 2026

addedInput schema / examples

Added value: +[
+  {
+    "odds": "+150"
+  },
+  {
+    "format": "auto",
+    "odds": "-200",
+    "stake": 100
+  }
+]

AddedSep 25, 2026
RemovedSep 25, 2026
First observedSep 25, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already mark readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context beyond that: it auto-detects the input format, reports detected_format, includes a direct REST fallback path, and explicitly notes 'Mathematics only; not gambling advice.' No contradiction with annotations.

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 description is long but well-structured with clear sections (when to use, when not, what it computes, inputs, examples, outputs, formula, fallback). It is front-loaded with usage context. The formulas and REST fallback are arguably beyond minimum needs but serve to eliminate ambiguity for a complex calculator with multiple outputs and auto-detection.

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?

The description covers all essential aspects for correct invocation: exact input formats, auto-detection rules, output fields, a direct REST fallback, and alternative tool routing. Given the tool's complexity and presence of an output schema, the description fully equips an agent to call it correctly without guessing.

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 description coverage is 100%, so parameters are fully documented with types, defaults, and format auto-detection rules. The description adds value by providing two complete JSON argument examples and mapping each parameter to its role, plus formulas that connect parameters to outputs. This exceeds the baseline 3 for high coverage.

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+resource: converts betting odds between American, decimal, and fractional formats, and computes implied probability and payout. It explicitly names three sibling tools it is not (parlay-odds, bet-expected-value, arbitrage-bet), clearly distinguishing it from related calculators.

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 explicitly states when to use ('when you have odds in one format and need the other two...') and when not to use, listing the alternative tool for each exclusion. It also instructs the agent to call the tool directly rather than deriving the answer from memory, leaving no ambiguity about invocation.

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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