infersports

Get Sharp Line

get_sharp_line

Read-only

Answer an odds question about a fixture in ONE call (natural language in, worked line out).

Resolves the fixture, picks the consensus line, the best price per outcome across books, and
de-vigged fair odds from the sharpest book — returning a ready-to-read ``summary`` plus the
full ``comparison``. Prefer this over chaining find_match → compare_lines.

Args:
    query: natural-language fixture, e.g. "Arsenal vs Man City" or a single team.
    market_type: "1x2", "asian_handicap" (default) or "totals".
    period: "full_time" (default) or "half_time".
    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.
    verbosity: "full" (default) or "terse". "terse" empties the per-book ``books`` array inside
        ``comparison`` to save tokens; the ``summary`` and worked numbers are kept either way.

On an ambiguous query, ``status`` is "ambiguous" and ``ask_user`` carries a disambiguation
prompt — do not assume a match; ask the user or re-call with a more specific query. A
``decision`` block (``safe_to_proceed`` / ``ask_user`` / ``next_action``) pre-computes the
go/no-go — branch on it instead of re-judging the result.

Input Schema

TableJSON Schema

Name	Required	Default
`date`	No
`query`	Yes
`sport`	No
`format`	No	decimal
`period`	No	full_time
`verbosity`	No	full
`market_type`	No	asian_handicap

TDQS

A4.6/5.0

Behavior5/5

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

Annotations indicate readOnlyHint=true, and the description confirms a single call that resolves and compares. It provides additional behavioral context: handling ambiguity via 'status' and 'ask_user', a 'decision' block for go/no-go, and the response structure (summary, comparison, decision). No contradiction with 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 well-structured with a one-sentence summary followed by bullet-pointed parameters. It is front-loaded with the core purpose. However, the Args section is somewhat verbose; a slightly more concise presentation could improve readability.

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 7 parameters, no output schema, and sibling tools, the description covers all parameters, explains the response structure (summary, comparison, decision), and addresses edge cases (ambiguity). It lacks explicit details about return values but provides enough context for an AI agent to understand the tool's behavior.

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%, so the description must compensate. It includes a detailed Args block explaining each parameter (query, market_type, period, format, sport, date, verbosity) with examples and defaults, adding significant value beyond the bare 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 description clearly states the tool answers an odds question about a fixture in one call, with natural language input and a worked line output. It specifies key features like resolving the fixture, picking consensus line, best price, and de-vigged fair odds, and distinguishes itself from chaining find_match and compare_lines.

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 advises to prefer this tool over chaining find_match and compare_lines. It also explains how to handle ambiguous queries (do not assume a match, ask user or re-call). While it gives clear context and a strong alternative, it could mention when not to use it (e.g., for detailed per-book lines rather than a summary).

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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