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sportskeeda_draft_picks

Get all historical NFL team draft picks from Sportskeeda as clean JSON, including public rows not initially visible, then filter by year, round, position, or player name.

Instructions

Get all historical NFL team draft picks from Sportskeeda. Reads the complete public draft JSON asset, not only the rows initially visible on the page. Filter by year, round, position or player name. Discover each team's exact filter values with draft-picks-options. Source history and latest available year are published by Sportskeeda and may lag the current season.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoCase-insensitive player-name substring, at most 100 characters
pageNoPage number, 1-1000; default 1
slugYesNFL team draft-picks page path without host
yearNoYear from draft-picks-options; omit for all years
roundNoExact round from draft-picks-options; historical special labels are accepted
per_pageNoRecords per page, 1-500; default 100
positionNoExact position from draft-picks-options

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.9

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses that it reads the complete public draft JSON asset rather than only page-visible rows, and warns that the source history/latest year may lag the current season. It omits pagination/rate-limit behavior, so not perfect.

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?

Five sentences, but each carries distinct information (scope, data source, filters, sibling pointer, data lag) and the core purpose is front-loaded. Slightly longer than strictly necessary but no filler.

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 7-parameter, no-annotation, no-output-schema tool, the description covers source behavior, filter semantics, the helper tool for valid values, and a data-freshness caveat. Pagination defaults are left to the schema, which documents them fully.

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 description coverage is 100%, so the baseline is 3. The description adds value by mapping the filterable fields (year, round, position, player name) and pointing to draft-picks-options as the source of exact accepted values for year/round/position.

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?

States a specific verb and resource ('Get all historical NFL team draft picks from Sportskeeda') and names the sibling tool draft-picks-options that exposes the exact filter values. An agent can distinguish this from its sibling without opening either schema.

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?

Gives clear context (filter by year, round, position, player name) and routes the agent to draft-picks-options for discovering valid filter values. It stops short of explicitly stating when NOT to use this tool, but the alternative is named with its 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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