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Prediction Markets Quant

by com.predictionmarketspicks

Explain a Player Ranking

explain_player
Read-only

Explain WHY the 2026 draft board ranks a player where it does, factor by factor: projection, floor/ceiling band, boom/bust week shape, and the three separate ranks a board row carries — our model's own positional rank, the market's ADP, and the published blend between them — plus the edge between model and market. Also states what the projection does NOT model (injuries, camp news, schedule). Free, no key. Use for "why do you have there", "explain ranking", "what's driving 's projection", "why is a sleeper/bust". For a plain outlook or a verdict rather than the reasoning, use player_outlook.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playerYesPlayer full name (e.g. "Bijan Robinson", "Jaxon Smith-Njigba").
scoringNoScoring format: standard, half_ppr (default), or ppr (full PPR). Overrides a platform preset. Works for Yahoo/ESPN/Sleeper defaults.
platformNoLeague platform — applies its default scoring, size, roster + best-ball settings (overridable by explicit scoring/teams). Underdog/DraftKings are best ball.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already provide readOnlyHint=true and openWorldHint=false. The description adds valuable context: it lists the factors explained, states what is NOT modeled (injuries, camp news, schedule), and mentions that it returns reasoning rather than a verdict. 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 relatively long but well-structured: front-loaded purpose, then list of factors, then exclusions, then usage examples, then alternative. Every sentence serves a purpose. A minor trim could improve conciseness, but overall efficient.

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 no output schema, the description covers what output to expect (factor-by-factor explanation) and lists the factors. It does not explicitly state the return format (e.g., text vs. JSON), but the factors are clear enough for an agent to understand the result. The tool is simple with no nested objects, so completeness is good.

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 baseline is 3. The description implies that 'player' is the key input and that 'scoring' and 'platform' are optional overrides, but this adds little beyond the schema's own parameter descriptions. No new parameter semantics are provided.

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's verb ('explain WHY') and resource ('2026 draft board ranks a player'), and lists specific factors it covers. It distinguishes itself from the sibling tool 'player_outlook' by stating when to use that alternative instead.

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 use cases ('why do you have <player> there', 'explain <player> ranking') and states when not to use it ('For a plain outlook or a verdict... use player_outlook'). It also mentions that the tool is free and requires no API key.

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.

Resources