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DanielTomaro13

sportsdata-mcp

espnfantasy_everything

Read-onlyIdempotent

Fetch all ESPN fantasy league data in one request: settings, teams, rosters, schedule, draft, and players. Use when a complete league snapshot is needed.

Instructions

UNDOCUMENTED mega-view (allon): settings + teams + rosters + schedule + draft + the whole player pool in ONE response. VERY LARGE (multi-MB) — prefer a targeted tool unless you genuinely need everything.

Returns: {settings, status, teams, members, schedule, draftDetail, players, playersHighlighted, creationInfo, lastUpdateInfo, lastAccessInfo} (~4 MB on a 10-team league)

Example: Everything about a league in one shot {"game": "ffl", "seasonId": 2018, "leagueId": 1234}

Auth: works without a key; ESPN_FANTASY_COOKIE unlocks more if set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameNoFantasy game code. One of: ffl, flb, fba, fhl, wfba.ffl
viewNoLeave as-is.
leagueIdYesLeague id. Required — part of the URL path.
seasonIdYesSeason year (>= 2018). Required — part of the URL path.
scoringPeriodIdNoScoring period to anchor roster/boxscore sections to.
Behavior5/5

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

Despite annotations indicating readOnly/idempotent, the description adds critical behavioral context: it is undocumented, returns a very large payload (~4 MB), lists the exact return keys, and explains auth requirements (works without key, cookie unlocks more). This goes well beyond the 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 somewhat lengthy but every line carries useful information: the mega-view contents, size warning, return keys, example, and auth note. It is front-loaded with the most important warning, though it could be tightened slightly.

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?

The description is quite complete for a tool with no output schema: it lists return keys, size, auth, and an example. However, it doesn't explain the structure of each returned section or how they relate to the targeted sibling tools, leaving minor gaps for an agent deciding between this and alternatives.

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 the schema already documents all parameters fully. The description does not add significant parameter semantics beyond the example call, so the baseline of 3 is appropriate.

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 it's a mega-view returning settings, teams, rosters, schedule, draft, and the player pool in one response. The verb 'mega-view' and enumeration of contents distinguishes it from targeted sibling tools like espnfantasy_league_settings or espnfantasy_rosters.

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?

Explicitly warns that the response is very large (multi-MB) and advises to prefer a targeted tool unless everything is genuinely needed. This provides clear context for when to choose this tool over alternatives, and the auth note adds usage nuance.

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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