infersports

Find Value Bets

find_value

Read-only

Find +EV value bets in a fixture — where a book's price beats the sharp fair line — in ONE call.

Resolves the fixture, de-vigs the sharp book (Pinnacle) at each line to get the fair price, then
flags every outcome whose best available price across books exceeds that fair price. DETECTION
ONLY: this surfaces the edge and which book holds it; it does NOT size stakes or link out to bet.

Args:
    query: natural-language fixture, e.g. "Netherlands vs Algeria" or a single team.
    markets: optional filter — any of "1x2", "asian_handicap", "totals" (default: all).
    period: optional — "full_time" or "half_time" (default: both).
    min_edge_pct: only report outcomes beating fair by at least this % (default 1.0).
    format: odds format — decimal | hk | malay | american | indonesian | probability.
    sport: optional filter — "football" or "basketball".
    date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures.

On an ambiguous query, ``status`` is "ambiguous" and ``ask_user`` carries a prompt — do not
guess. Needs the sharp book to de-vig; on the Free tier ``note`` flags that fair is approximate.

Input Schema

TableJSON Schema

Name	Required	Default
`date`	No
`query`	Yes
`sport`	No
`format`	No	decimal
`period`	No
`markets`	No
`min_edge_pct`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses several key behaviors: it is detection-only and 'does NOT size stakes or link out to bet'; on ambiguous queries it returns 'status'=>'ambiguous' with 'ask_user' and instructs 'do not guess'; it depends on the sharp book and on the Free tier flags fair as approximate. These details significantly exceed the annotation and help the agent understand edge cases.

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 well-structured: a one-sentence front-loaded purpose, a concise process explanation, a highlighted 'DETECTION ONLY' note, a clear Args list, and a final behavioral note. It is somewhat long but every sentence earns its place; no redundancy. The top score is held back only by minor verbosity in the process explanation that could be tightened.

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?

Despite lacking an output schema, the description covers all necessary call information: how the tool works, all parameters with defaults, the ambiguous-query response, the Free tier constraint, and the detection-only scope. There is nothing an agent needs to invoke it correctly that is missing. It is complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero descriptions, so the description carries the full burden. It explains each of the 7 parameters in the Args block: query as natural-language fixture, markets with allowed values, period with allowed values, min_edge_pct with meaning, format with options, sport with options, and date for disambiguation. This adds substantial meaning beyond the bare schema types and defaults.

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: 'Find +EV value bets in a fixture' and elaborates the exact detection mechanism ('de-vigs the sharp book... flags every outcome whose best available price across books exceeds that fair price'). It also clearly distinguishes its scope from betting actions via 'DETECTION ONLY'. This is unambiguous and differentiates it from siblings like find_arbitrage or get_sharp_line.

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

Usage Guidelines4/5

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

The description gives clear context on when to use it (to find value bets in one call) and adds a caution ('DETECTION ONLY' – not for sizing stakes). However, it does not explicitly name alternative tools or state 'use this instead of X'. The 'in ONE call' phrasing implies comprehensiveness, but no explicit when-not guidance is provided, so it doesn't fully reach the top score.

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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