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DanielTomaro13

sportsdata-mcp

supercoach_players

Get a complete snapshot for every player in a round: price, projection, averages, ownership, positions, news, and upcoming matchups. Pass round and sport to retrieve forward-looking data.

Instructions

THE feed — every player's full snapshot for one round: price + price_change, avg/avg3/avg5, the ppts1 projection, owned %, positions, availability, dated news and the next-three-opponents matchup context. LARGE (~1–3 MB; 174–1032 players depending on sport). Pass round (from supercoach_settings.next_round) for a forward snapshot; loop round=1..current_round with embed=player_match_stats to build a per-round score series. The embed set is CLOSED — only positions, player_stats, player_match_stats, notes, odds add data (anything else is ignored).

Returns: array of {id, first_name, last_name, team:{name, abbrev}, team_id, played_status:{status}, injury_suspension_status_text, positions:[{position, position_long}], notes:[{note, created_on}], player_stats:[{price, price_change, total_price_change, avg, avg3, avg5, ppts1, ppts, owned, total_games, total_points, opp:{abbrev}, opph, oppavg, ven:{name}, venavg, opp1, opp2, opp3, total_<sport stats…>}], player_match_stats:[{games, points}]} — USE ppts1 (real projection, ≈avg, present for afl/nrl; falls back to avg when absent, e.g. nba). AVOID ppts (erratic). In mode=draft each player also carries top-level predraft_rank + player_stats.position_ranks. LARGE.

Example: AFL round-16 forward snapshot (price + projection + matchup)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoclassic
yearYes
embedNopositions,player_stats,notes
roundNo
sportYes
Behavior4/5

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

No annotations exist, so the description carries the burden. It discloses large data size (~1-3 MB), closed embed set, and data quality warnings (avoid ppts, use ppts1). This is sufficient for behavioral expectations.

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 front-loaded with core purpose and structured with sections. While comprehensive, a few sentences could be trimmed without losing essential information.

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?

Given no output schema, the description provides a detailed return structure and example. It covers size, embed behavior, and field reliability. Missing error handling or prerequisites beyond round, but adequate.

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 0%, but the description explains round usage, embed values, and mode effect. However, required parameters 'sport' and 'year' are not explained, leaving a 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 clearly states the tool provides 'every player's full snapshot for one round' with specific fields listed. It uses strong verbs and distinguishes from sibling tools like supercoach_player by indicating it returns bulk data.

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 advises passing 'round' from settings and looping with embed for historical data. It also warns against using 'ppts' and clarifies embed constraints. However, it does not explicitly state when not to use this tool versus alternatives.

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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