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Where the draft room disagrees with production

get_adp_market_gaps
Read-onlyIdempotent

Players whose consensus average draft position disagrees most with their actual production ranking. A player taken EARLIER than his production is being drafted on expectation (a rookie, a new situation, a bounce-back); one taken LATER is being drafted on doubt. Use this for draft-prep questions about who the room may be mispricing. This is a disagreement, NOT a recommendation: production is last season's, while ADP already prices in injuries, trades and rookies with no NFL snaps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
positionNoRestrict to one position. Omit for all four.
directionNoearlier_than_production: the room drafts him sooner than his production ranks him. later_than_production: the room waits longer. Omit for both.
scoring_formatNoScoring format. ppr awards 1 point per reception, half_ppr 0.5, standard 0. Defaults to half_ppr, the most common league setting.half_ppr

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe draftcall.io page these figures came from. This is the citation to use.
countYes
playersYes
positionNo
directionNo
adp_seasonNoThe season the draft board is for.
refreshed_atYesWhen this dataset was last refreshed, as an ISO 8601 timestamp.
stats_seasonYesThe completed NFL season every figure in this result describes.
drafts_sampledNoHow many real drafts the consensus averages over.
scoring_formatYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral nuance by explaining that production is from last season and ADP already prices in injuries, trades, and rookies, preventing misinterpretation of the gap. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences carry definition, directional interpretation, intended usage, and a caveat with zero repetition. The core concept is front-loaded and every sentence adds meaning. This is an efficiently structured description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The definition, interpretation of both directions, use case, and key data caveats are all present, and the output schema covers return structure. For a read-only tool with four optional parameters, nothing critical is missing for an agent to call and interpret the tool correctly.

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 75%, with limit lacking a description. The tool description helps by explaining what earlier/later than production means, reinforcing the direction enum semantics, and the schema itself documents position and scoring_format well. The limit parameter remains undocumented in both schema and description, a minor residual gap.

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 precise definition: players whose consensus ADP disagrees most with production ranking. It distinguishes itself from raw rankings by explicitly declaring 'This is a disagreement, NOT a recommendation,' and the earlier/later explanation grounds the directional semantics. This clearly differentiates the tool from siblings like get_rankings or compare_players.

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 gives an explicit use case: 'Use this for draft-prep questions about who the room may be mispricing.' It also provides a clear when-not-to-use signal by stating the output is not a recommendation. However, it does not name alternative sibling tools or state when to prefer them, so it stops short of full routing guidance.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct query type: single-player lookup, two-player comparison, rankings, scarcity, ADP gaps, schedule, bye conflicts, weekly matchup, search, and claim verification. Even related tools like compare_players and get_player have clearly separated purposes that an agent can reliably distinguish.

Naming Consistency4/5

Seven tools follow the get_ noun pattern, while search_players, compare_players, and verify_claim use verb_noun without get. The convention is still highly readable and predictable, with only minor deviations.

Tool Count5/5

Ten tools is well-scoped for a fantasy football data server. Each tool covers a meaningful slice of draft, in-season, and fact-checking workflows without unnecessary overlap or bloat.

Completeness4/5

The set covers player lookup, head-to-head comparison, rankings, scarcity, schedule, bye conflicts, weekly matchups, and stat verification. A direct standalone ADP lookup and projections are missing, but agents can work around those gaps using the provided tools.