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yahoo_sports_draft

Retrieve Yahoo Sports NFL or NBA draft results, mock drafts, big boards, and news, along with live tab lists and browser filter selectors for building draft pages.

Instructions

Yahoo Sports NFL and NBA Draft hub. Returns Yahoo's Drafted Players, Mock Draft, Big Board, or News tab for NFL or NBA, together with the live tab list and exact client-side round/team/position/school/attribute/board selectors shown by that page. The selectors filter data in the browser, so this endpoint returns the complete upstream page data and exposes filter values without applying server-side filters.

Input Schema

NameRequiredDescriptionDefault
viewNoYahoo Draft page: results, mock, big-board, or news; defaults to results
leagueYesYahoo draft league; use yahoo-sports-leagues (draft=true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.18

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden and does disclose a genuinely non-obvious trait: filtering happens client-side, so the endpoint returns the complete upstream page data and exposes filter values without applying them. That prevents a wrong expectation of filtered results. It says nothing about response size, pagination, or auth needs, which keeps it below a 5.

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?

Three sentences, front-loaded with the resource and the returned views, then the selectors, then the filtering caveat. Dense but each sentence carries information; the third sentence is long but earns its place by explaining the no-server-filter behavior.

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?

No output schema exists, so the description must convey the return shape, and it does: page data for the chosen tab plus the live tab list and exact selectors. Combined with full schema coverage for the two parameters, an agent has enough to call it correctly; only response volume/format details are missing.

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 100% and both parameters are enums, so the baseline is 3; the description adds value by mapping the enum value 'results' to the page label 'Drafted Players' and grouping the four views semantically. League coverage (NFL/NBA) is restated from the schema rather than extended.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names the concrete resource (Yahoo Sports NFL and NBA Draft hub) and enumerates the four sub-views it can return (Drafted Players, Mock Draft, Big Board, News). It stops short of naming which sibling to use instead (e.g. yahoo_sports_player or yahoo_sports_news), so differentiation comes from the resource framing rather than explicit routing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: an agent can infer it retrieves a draft page for a league, and the note that selectors filter in the browser tells it not to expect server-side filtering. There is no explicit when-to-use, when-not-to-use, or alternative-sibling guidance, so the agent must infer the fit.

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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