Line Variance Data

get_sharp_consensus

Computes the de-vigged, cross-bookmaker consensus fair win probability for every surveyed side/market via the deterministic multi-way vig-stripping engine, reporting each side's raw vigged consensus alongside its fair (sharp) probability. Covers the five professional leagues (mlb, nfl, nba, wnba, nhl). Omit sport for the all-league aggregate. Collegiate and NCAA rows are never returned.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_sharp_consensus"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Maximum number of results to return.
`sport`	No	Optional professional-league filter. Case-insensitive. Allowed values: mlb, nfl, nba, wnba, nhl. Omit to return the aggregate across all five active pro leagues. Collegiate and NCAA rows are never returned.
`league`	No	Legacy alias for sport. Case-insensitive professional league: mlb, nfl, nba, wnba, nhl. Ignored when sport is also set. Omit both sport and league for the all-league aggregate.
`min_books`	No	Minimum distinct books required to report a side.
`market_name`	No	Optional exact market name filter, e.g. 'Moneyline'.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 9, 2026

TDQS

A3.5/5.0

Behavior4/5

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

No annotations exist, so the description carries the full burden, and it does disclose meaningful behavior: the deterministic vig-stripping method, that two values (raw vigged consensus and fair probability) are reported per side, the five-league coverage boundary, and the hard exclusion of collegiate/NCAA rows. Gaps remain around ordering, pagination behavior of the limit parameter, and explicit read-only status, which keeps it from a 5.

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?

Three sentences with the core purpose front-loaded, followed by coverage and exclusion constraints. Minor waste: the NCAA/collegiate exclusion is stated in the description and again in the sport parameter schema, and the 'deterministic multi-way vig-stripping engine' phrasing is jargon-heavy.

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?

With no output schema, the description usefully compensates by explaining what each returned side contains (raw vigged consensus plus fair probability). Combined with the league coverage and exclusion rules, an agent has enough to call it correctly, though result ordering and empty-result behavior are unstated.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented, including the league/sport alias precedence and the NCAA exclusion. The description largely restates the schema's sport semantics and adds little new parameter meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and a well-defined resource: a de-vigged, cross-bookmaker consensus fair win probability computed by a multi-way vig-stripping engine, including that both raw vigged and fair probabilities are reported. This is far more specific than the name alone. It does not name or contrast with its siblings get_arbitrage_opportunities and get_line_variance, so it falls short of a 5.

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

Usage Guidelines2/5

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

The description explains scoping ('omit sport for the all-league aggregate', 'collegiate and NCAA rows are never returned') but never says when an agent should reach for this tool instead of get_arbitrage_opportunities or get_line_variance. Those distinctions are left entirely to inference from the sibling names.

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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