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Prediction Markets Quant

by com.predictionmarketspicks

NFL Win Probability Calculator

nfl_win_probability
Read-only

Turn an NFL point spread and game total into win probability, projected score, cover probability, and over/under probability — using the PredictionMarketsPicks scoring-margin model. Provide the spread (home-favored = negative, e.g. -6.5) and optional total, OR two team abbreviations to auto-derive the spread from the power ratings. Free, no key. Use for "NFL win probability from the spread", "what does a -7 spread mean", "who wins Chiefs vs 49ers", "NFL score prediction".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNoGame total (over/under points). Defaults to the league baseline (44) if omitted.
spreadNoPoint spread from the home team's perspective — home favored is NEGATIVE (e.g. -6.5). Provide this OR homeTeam+awayTeam.
awayTeamNoAway team — same formats as homeTeam (SF, 49ers, San Francisco 49ers). Used with homeTeam.
homeTeamNoHome team — abbrev (KC), nickname (Chiefs), full name (Kansas City Chiefs) or city (Kansas City) all work. Auto-derives the spread from PMP power ratings.

TDQS

A4.4/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true (safe read) and openWorldHint=false (closed world). The description adds the free/no-key detail and the model source, but doesn't elaborate on performance limitations, data freshness, or edge cases (e.g., postseason, ties). With annotations covering safety, a 3 is appropriate for missing behavioral context.

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 two sentences with clear verb-first structure and front-loaded outputs. Every phrase adds value, and example queries are appended after the core purpose. No wasted words.

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 the complexity of this tool (4 optional params, no output schema, no enums), the description covers input modes and outputs adequately. Some gaps: doesn't specify return format for projected score or probability values, nor handling of invalid team names. With no output schema, more detail on return structure would push to 5.

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

Parameters4/5

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

Schema coverage is 100% and descriptions already explain each parameter well. The tool description adds value by explaining the two usage modes (spread+total vs. homeTeam+awayTeam) and the sign convention for spread (negative when home favored), which enhances schema meaning. Lacks detail on how total default is derived but that is minimal.

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 that the tool computes win probability, projected score, cover probability, and over/under probability from a point spread and game total, using the PredictionMarketsPicks model. It distinguishes itself from siblings by being the only tool dedicated to NFL win probability calculation.

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?

The description provides explicit guidance on when to use the tool, including example queries like 'NFL win probability from the spread' and 'who wins Chiefs vs 49ers'. It also explains two usage modes: direct spread+total input or team abbreviations to auto-derive spread, with no discussion of when not to use it (not needed given clear purpose).

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

A3.9/5.0
Disambiguation4/5

Most tools target distinct workflows with explicit usage hints, such as best_available vs who_do_i_draft vs compare_players. The main risk is the cluster of 'edge' tools—calculate_ev, scan_mispricings, edge_alerts, nfl_edge, nfl_prop_edge—but descriptions narrow them by venue, asset, and scope well enough.

Naming Consistency3/5

All names use lowercase snake_case, but there is no stable verb_noun pattern: some are verbs (calculate_ev, scan_mispricings), many are noun phrases (draft_board, market_pulse, nfl_edge), and one is a full question (who_do_i_draft). The nfl_* prefix adds structure, but overall the convention is mixed though still readable.

Tool Count3/5

24 tools is on the heavy side and the set spans two fairly distinct domains: prediction-market edge tools and fantasy-football draft tools. Most tools serve a distinct function, but several 'edge' tools and the eight fantasy tools could feel redundant if consolidated.

Completeness4/5

The set covers the core prediction-market workflow: base rates, Bayesian updates, EV, Kelly sizing, mispricing scans, arbitrage, and vertical signals, plus a full fantasy-draft surface. Notable gaps are generic market/price lookup and portfolio-level tracking, but agents can work around these.

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