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
`links`	No
`result`	Yes
`request`	Yes
`sources`	No
`success`	Yes
`version`	No
`freshness`	No
`timestamp`	Yes
`next_actions`	No
`service_version`	Yes	Deployed Calcgrid implementation version used for the result.

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 27, 2026

addedInput schema / properties / odds / maxLength
```
Added value: +20
```

addedOutput schema / properties / service_version

Added value: +{
+  "description": "Deployed Calcgrid implementation version used for the result.",
+  "type": "string"
+}

changedOutput schema / required

Previous value: -[
-  "success",
-  "request",
-  "result",
-  "timestamp"
-]New value: +[
+  "success",
+  "service_version",
+  "request",
+  "result",
+  "timestamp"
+]

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 declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds meaningful behavioral context: auto-detection of input format, exact computed outputs, the underlying formulas, and a direct REST fallback. It also clarifies this is mathematics only and not gambling advice, with no contradiction of the 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 longer than average, but it is well-structured with clear sections for usage, exclusions, inputs, outputs, and formulas. It is front-loaded with the primary use case and exclusion guidance. The formula block is dense but earned because it precisely disambiguates the conversion semantics.

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?

Given the tool's moderate complexity, the description covers input formats, parameter semantics, output fields, examples, formulas, REST fallback, and documentation link. The rich output schema plus this description leaves no significant gap for an agent to call the tool correctly.

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 the baseline is 3. The description still adds value by showing complete JSON examples, explaining what stake means in relation to profit and total return, and providing the conversion formulas that define how each parameter is interpreted.

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 uses a specific verb and resource: converting betting odds between American, decimal, and fractional formats while reporting implied probability and payout. It also explicitly names sibling tools it is not (parlay-odds, bet-expected-value, arbitrage-bet), making differentiation clear.

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 states exactly when to use the tool ('when you have odds in one format and need the other two') and gives explicit exclusions with alternative tool names for combined prices, expected value, and bookmaker margin. This leaves no ambiguity about routing.

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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