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

TDQS

A4.7/5.0

Behavior5/5

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

Annotations provide readOnlyHint=true, and the description does not contradict that. The description adds significant behavioral context: it resolves the fixture, de-vigs the sharp book, flags outcomes, and describes edge conditions (ambiguous query, Free tier note). This goes well beyond the annotation.

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?

The description is concise and well-structured: a one-line summary, a paragraph explaining the process, a note on detection-only, and a clear parameter list. Every sentence adds value without redundancy.

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

Completeness4/5

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

Given no output schema and 7 parameters, the description covers inputs thoroughly and explains output behavior (flaggings, ambiguous status). It lacks explicit return structure details, but overall it is quite complete for a detection tool.

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?

Schema description coverage is 0%, but the description provides detailed explanations for all 7 parameters in the 'Args' section, adding meaning beyond the schema types and titles. For example, 'query: natural-language fixture' and 'min_edge_pct: only report outcomes beating fair by at least this %.'

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 starts with a clear verb and resource: 'Find +EV value bets in a fixture — where a book's price beats the sharp fair line — in ONE call.' It distinguishes from sibling tools like 'find_arbitrage' by specifying value betting vs. arbitrage.

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 explicitly states 'DETECTION ONLY: this surfaces the edge and which book holds it; it does NOT size stakes or link out to bet,' providing clear when-to-use and when-not-to-use guidance. It also warns about ambiguous queries and Free tier limitations. However, it does not explicitly compare to siblings.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: compare_lines contrasts bookmakers, compare_prob evaluates external probabilities, find_arbitrage detects arbitrage opportunities, find_match resolves fixtures, etc. Even related tools like get_sharp_line (one-call line) and compare_lines (event_id-based comparison) are complementary rather than overlapping. No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_lines, find_match, list_bookmakers). Verbs like compare, find, get, list, scan, score are uniform. No mixing of conventions or vague names.

Tool Count5/5

With 17 tools covering fixture discovery, odds retrieval, comparisons, value/arb detection, line movement, results, and slate scanning, the count is well-scoped for a sports betting odds API. No unnecessary tools, and the set feels complete without being bloated.

Completeness5/5

The tool surface covers the full lifecycle: find matches (find_match, list_events, list_today_matches), get odds (get_match_odds, get_sharp_line, compare_lines), detect value/arb (find_value, find_arbitrage, scan_slate), analyze lines (get_opening_line, explain_handicap, score_prob), and retrieve results (get_result, list_results). No obvious gaps for the stated read-only odds analysis domain.

Resources

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