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

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 7, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations only provide readOnlyHint, but the description adds substantial behavioral context: it explains the decision block (safe_to_proceed / ask_user / next_action), what happens on ambiguity, and how verbosity='terse' changes the books array output. It also clarifies output structure (summary/comparison) beyond the annotation, with no contradiction.

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 front-loaded with the one-line purpose, then organized into parameter details and behavioral notes. It is longer than average, but that length is justified given 7 parameters with zero schema coverage and the need to explain disambiguation and verbosity behavior. No sentence is pure filler.

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 7-parameter tool with no output schema, this description is remarkably complete: it tells the agent what inputs to use, what outputs to expect, how to handle ambiguity, and which block to branch on. It even accounts for the output size optimization of 'terse'. The one thing not covered (e.g., error status when no fixture is found) is minor given the detail present.

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 bears full responsibility for parameter semantics. It documents all 7 parameters with default values, allowed values (market_type, period, format, verbosity), examples for query, and the practical effect of verbosity. This is strong compensation for an otherwise empty 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 names a specific verb and resource: 'Answer an odds question about a fixture in ONE call' and enumerates exactly what it computes (consensus line, best price, de-vigged fair odds, summary, comparison). It also distinguishes itself from the sibling chain find_match → compare_lines, so an agent can separate it from related tools.

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?

It explicitly prescribes when to use this tool: 'Prefer this over chaining find_match → compare_lines.' It also gives a clear conditional behavior for ambiguous queries: 'do not assume a match; ask the user or re-call with a more specific query.' This is concrete, actionable when/when-not 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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