infersports

Scan Slate

scan_slate

Read-only

Scan today's whole slate in ONE call — each fixture with honest status + value/arb signal.

The batch alternative to looping find_match → get_sharp_line per match. Returns every fixture in
the filter with its status (finished is excluded from "live"), live score/clock, and a
pre-computed value/arb signal; value/arb matches are sorted to the top and the list is truncated
to ``limit`` (so truncation drops the quiet ones). Line movement is NOT included (that needs the
opening lookup) — drill into a single fixture with get_opening_line. DETECTION ONLY / read-only.

Args:
    sport: optional filter — "football" or "basketball".
    status: optional filter — "live" | "scheduled" | "finished".
    league: optional league filter — a name (fuzzy-matched, e.g. "World Cup") or an external id (lg_…).
    markets: optional — limit the value/arb scan to "1x2"/"asian_handicap"/"totals" (default all).
    period: optional — "full_time" or "half_time" (default both).
    min_edge_pct: value threshold for the per-match signal (default 1.0).
    min_margin_pct: arbitrage threshold for the per-match signal (default 0.0).
    only_signal: if true, return only fixtures that have a value or arb signal.
    format: odds format — decimal | hk | malay | american | indonesian | probability.
    limit: max entries to return, signal-first (default 20, max 100).

Input Schema

TableJSON Schema

Name	Required	Default
`limit`	No
`sport`	No
`format`	No	decimal
`league`	No
`period`	No
`status`	No
`markets`	No
`only_signal`	No
`min_edge_pct`	No
`min_margin_pct`	No

TDQS

A4.9/5.0

Behavior5/5

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

Aligns with readOnlyHint annotation, adding details: truncation drops quiet fixtures, sorting by signal, exclusion of finished from 'live' status, and pre-computed signals. No contradictions.

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?

Well-structured with front-loaded summary, sibling context, behavioral details, then parameter list. Slightly verbose but every sentence adds value; could be tightened slightly.

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 10 parameters, no output schema, and minimal annotations, the description covers all essential aspects: purpose, usage context, parameter meanings, behavioral traits, and exclusions. Return values are implied (status, score/clock, signal). Complete for the tool's 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?

Schema coverage is 0%, but the description provides detailed explanations for all 10 parameters, including allowed values (e.g., sport: football/basketball), defaults, and constraints (limit max 100). This fully compensates for the lack of schema descriptions.

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?

Clear verb+resource: 'Scan today's whole slate' with specific scope (fixtures, value/arb signal). Distinguishes from siblings by stating it's the batch alternative to looping find_match/get_sharp_line and contrasts with get_opening_line for line movement.

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?

Explicitly states when to use (scan today's slate in one call) and when not to (for line movement, use get_opening_line). Names specific sibling tools (find_match, get_sharp_line) as alternatives, providing clear decision context.

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.

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