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Prediction Markets Quant

by com.predictionmarketspicks

NFL Power Ratings (PWR)

nfl_power_ratings
Read-only

The PredictionMarketsPicks NFL power ratings — PWR for all 32 teams: points per game above an average team on a neutral field, where PWR = Off PR + Def PR + ST PR. Includes each team's rank and tier. Free, no key. Use for "best NFL teams by power rating", "NFL power rankings 2026", "is Baltimore overrated", "how good is Kansas City", "NFL team ratings".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamNoOptional team abbreviation (e.g. "KC", "SF", "LAR") — returns just that team's rating + rank.
tierNoOptional: only teams in this tier — one of "Elite" (PWR ≥+7), "Contender" (≥+4), "Playoff" (≥+1), "Average" (≥−2), "Below Avg" (≥−5), "Rebuild" (<−5). A tier with no teams at current ratings returns an empty board, not an error.
limitNoMax teams to return, best rating first (default 32 = full board).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds meaningful context beyond annotations: it states 'Free, no key' (no authentication required) and explains the PWR formula and that tiers with no teams return an empty board (from the parameter description). 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?

The description is compact but informative, including the metric definition, what's included (rank and tier), free/no-key note, and usage examples. It is front-loaded with the core purpose and avoids unnecessary fluff, though it could be slightly condensed without losing meaning.

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 there is no output schema, the description does not explicitly describe the response format (e.g., list of teams with PWR, rank, tier). However, it implies the content ('all 32 teams', 'each team's rank and tier') and explains the tier classification scheme. This is adequate for a read-only ratings lookup, but a more explicit output description would be more complete.

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?

All three parameters (team, tier, limit) have detailed descriptions in the input schema, covering team abbreviations, tier ranges, and limit bounds. The description does not add significant parameter-specific value beyond what the schema already explains, so a baseline score of 3 is appropriate.

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 provides NFL power ratings (PWR) for all 32 teams, defines the metric formula (PWR = Off PR + Def PR + ST PR), and includes rank and tier. It distinguishes this from sibling tools like nfl_win_probability and nfl_prop_edge by focusing on overall team strength ratings.

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 provides explicit usage scenarios: 'Use for "best NFL teams by power rating"', 'NFL power rankings 2026', 'is Baltimore overrated', 'how good is Kansas City', 'NFL team ratings'. It effectively communicates the tool's domain, though it does not explicitly state when not to use it or compare to alternatives.

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
Disambiguation3/5

Many tools have clearly distinct domains (fantasy vs NFL vs commodities vs general mispricings), but the 'edge' family is crowded: calculate_ev, scan_mispricings, edge_alerts, find_arbitrage, commodity_edge, nfl_edge, and nfl_prop_edge all surface pricing edges in overlapping ways. Fantasy tools like best_available and who_do_i_draft also have very similar mid-draft recommendation purposes, though their inputs differ.

Naming Consistency4/5

All tool names use lowercase snake_case and are readable, but they mix verb_noun patterns (calculate_ev, compare_players, scan_mispricings) with noun-phrase names (adp_market_gaps, edge_alerts, kelly_size, market_pulse). The style is consistent enough that an agent can predict the convention, with only minor deviations from a strict verb-first pattern.

Tool Count3/5

23 tools is on the heavy side for a single MCP server, though the scope is genuinely broad: prediction-market edge detection, position sizing, probability math, and fantasy football draft tools. It is not bloated enough to feel chaotic, but several tools could be consolidated or are tier-gated variants of the same underlying data.

Completeness4/5

The fantasy football surface covers the draft lifecycle well: rankings, player outlooks, comparisons, ADP gaps, and in-draft recommendations. The prediction-market side covers edge detection, EV, Kelly sizing, base-rate comparison, and arbitrage discovery, though it lacks direct market-price fetching or portfolio tracking—minor gaps that users can work around by supplying prices themselves.

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