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Best bookmaker price for a bet

find_best_price
Read-only

Find which bookmaker pays the most for one specific bet when VoxOdds has a configured live sportsbook feed. The registry is a directory, not live coverage. Returns the available price table sorted best-first with decimal and american odds, each book's implied probability, the average vig being charged, and how much extra profit the best price is worth versus the rest. Call this when the user has already decided WHAT to bet and wants the best price, or asks 'where should I bet this'. Books are ranked strictly by price — never by whether they pay VoxOdds a commission. Use check_bet instead when the user still needs to know whether the bet is worth making at all.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideYesThe side the user has decided to back
eventYesThe event in plain words
sportNoSport key such as 'basketball_nba', or 'upcoming'upcoming

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / event / description
      Added value: +"The event in plain words"
    • addedInput schema / properties / side / description
      Added value: +"The side the user has decided to back"
    • addedInput schema / properties / sport / description
      Added value: +"Sport key such as 'basketball_nba', or 'upcoming'"
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond readOnlyHint/openWorldHint, the description discloses a material data-availability caveat ('registry is a directory, not live coverage'), the return shape (best-first table with decimal/american odds, implied probability, average vig, marginal profit), and an integrity guarantee ('ranked strictly by price — never by whether they pay VoxOdds a commission'). These are traits the annotations cannot express.

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 guiding constraints (feed requirement, ranking rule, sibling alternative) are front-loaded and every sentence carries information. It runs a few sentences long and re-enumerates return fields that an existing output schema already covers, which is mild redundancy.

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 3-parameter read-only lookup with an output schema, nothing an agent needs is missing: the usage trigger, the alternative, the data-coverage caveat, and the ranking integrity rule are all present. Return values are fully covered by the output schema and helpfully summarized anyway.

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% and all three parameters (event, side, sport) are documented in the schema, so the baseline is 3. The description adds only the framing that this targets 'one specific bet', no syntax or format detail 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 first sentence names a specific verb (find), a specific resource (which bookmaker pays the most for one bet), and a precondition (VoxOdds has a configured live sportsbook feed). It also distinguishes itself from the sibling check_bet by contrast, so an agent can route between them without opening either schema.

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?

Explicit trigger conditions are given ('when the user has already decided WHAT to bet', 'where should I bet this') plus a named alternative and the condition that selects it ('Use check_bet instead when the user still needs to know whether the bet is worth making at all'). This is textbook when/when-not/alternative guidance.

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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