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Prediction Markets Quant

by com.predictionmarketspicks

ADP Market Gaps

adp_market_gaps
Read-only

Find the 2026 fantasy players whose Average Draft Position swings most between platforms — consensus vs ESPN, Sleeper, Yahoo, Underdog, etc. A wide gap (e.g. "consensus RB18 but ESPN drafts him RB30") is a platform-specific value: grab him where he goes latest. Free without a key (a capped slice). One email unlocks a deeper list; Pro unlocks the full per-platform breakdown. Use for "ADP differences by platform", "where is a player cheapest", "ADP arbitrage", "who falls on ESPN vs Sleeper". Filter by position (QB/RB/WR/TE/FLEX).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax players to return, widest gap first (default 25).
scoringNoScoring format: standard, half_ppr (default), or ppr (full PPR). Overrides a platform preset. Works for Yahoo/ESPN/Sleeper defaults.
positionNoOptional position filter (FLEX = RB/WR/TE).

TDQS

A4.1/5.0
Behavior4/5

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

With readOnlyHint=true, the description still adds useful behavioral context: free usage is 'a capped slice', deeper list requires email, Pro unlocks all-platform detail. It also frames the result as ranking players 'a wide gap', which helps the agent interpret how to use it. No contradiction of 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?

It is friendly and scannable, with the core action in the first sentence and the richer use-case examples later. The tier/free notes are slightly marketing-adjacent but fence-ear every sentence and are useful for operational expectations.

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?

For a simple parameter-light tool with no output schema, it tells the user what it returns (an ordered list of biggest ADP gaps), how to filter, and what the free tier limits are. The only missing detail is a precise enumeration of the response fields, but the example conflict 'consensus RB18 bass ESPN ABHIM' conveys the shape.

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%: `limit`, `scoring`, and `position` are all explained in the input schema. The description only reiterates the position filter and adds a platform gap context, which in place-parameter semantics is adequate but not compensation.

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 opens with a specific verb-resource pairing: 'Find the 2026 fantasy players whose Average Draft Position swings most between platforms.' It distinguishes itself from siblings by naming concrete sources (consensus vs ESPN, Sleeper, Yahoo, Underdog) and providing explicit example use cases like 'where is a player cheapest' and 'who falls on ESPN vs Slepper'.

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?

It gives clear context: use for ADP platform differences, cheapest-player identification, ADP arbitrage, and who-falls-on-specific-platform. It does not explicitly name alternatives or say when NOT to use a sibling, so it stops short of a perfect 5.

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