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augforce

hockey-card-analyst

by augforce

assess_player

Assess a hockey player card by extracting metrics and returning overall tier, strengths, weaknesses, deployment, trajectory, caveats, and a summary.

Instructions

Assess one player's card: overall tier, strengths, weaknesses, deployment, trajectory, caveats, and a one-line summary.

YOU (Claude) read the card image and extract the fields; this server never sees the image — pass it clean structured data. The server maps percentiles to tiers, picks the strengths/weaknesses, and attaches the caveats. Narrate from what it returns, not from how you think WAR models generally work — the result carries the correct framing inline.

card is one JSON object. Skater (forward or defenseman): name, position (C/LW/RW/L/R/F; D for a defenseman), [team, age], [toi_role, cap, competition, teammates], ev_offense, ev_defense, pp (null if NA), pk (null if NA), finishing, penalties, proj_war_pct, [goals, first_assists], [war_pct_trend: list of {season, value}]. Goalie (no position; has role): name, [team, age], [gp_pct], role (Starter/1A/1B/Backup), [cap], proj_war_pct, even_strength, penalty_kill, high_danger, med_danger, low_danger, quality_starts, excellent_starts, bad_starts, rebound_control, consistency, [war_per60_trend, sv_vs_xsv_trend].

All percentiles are integers 0-100, already oriented so higher is better — including goalie Bad Starts and Consistency; do NOT invert them. A role the player doesn't have (e.g. no PK) is null/NA, not 0 — NA is an absence of role, not a weakness. Some cards (e.g. a UFA's) print a blank Age and show no team: omit team/age rather than guessing, and never infer the team from the jersey or photo.

Guardrails: never invent a stat that isn't on the card; a defenseman's finishing is descriptive only (excluded from his WAR); surface the returned caveats rather than dropping them.

Narrate the returned STRUCTURE, not your own regrouping: "strengths" and "weaknesses" are exactly the returned lists. The descriptive reads (goals, first assists) are supporting color the engine deliberately keeps OUT of the value verdict — you may cite them as descriptive color, but NEVER present them as strengths, weaknesses, or part of the WAR case.

Scope: this tool interprets ONLY what's on the card. Anything the card can't see — trades, contracts, current team, roster context, who leads a team in scoring, recent game stats — is out of scope and it does not provide it. If you fill such a gap from outside the card (e.g. the web), mark that content clearly as NOT from the card and note it should be independently verified; keep card-derived verdicts (traceable to the numbers) visibly separate from outside context (unverified).

After presenting this assessment, ALWAYS close your answer by offering the user a downloadable PDF report of it — generated with the render_report tool (kind "assess_skater" or "assess_goalie"), passing THIS result verbatim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels. It explains the tool's internal behavior: mapping percentiles to tiers, picking strengths/weaknesses, attaching caveats. It also warns against inverting goalie stats, handling NAs, and not inventing stats. This fully informs the agent of the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is extremely verbose, containing lengthy meta-instructions for the AI agent about how to present output, guardrails, and narratives. While structured, it goes far beyond concise documentation, making it harder to parse quickly.

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?

Given the complexity of the tool (single parameter with elaborate nested object, output schema exists), the description is exceptionally complete. It covers input structure, processing logic, and output formatting, leaving no significant gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% with only 'card' as an untyped object. The description compensates by exhaustively documenting the expected structure for skaters and goalies, including all fields and their meanings. This is a complete and detailed definition of the parameter's semantics.

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?

The description states the tool's purpose clearly: 'Assess one player's card: overall tier, strengths, weaknesses, deployment, trajectory, caveats, and a one-line summary.' It distinguishes itself from siblings like compare_players by focusing on a single player's assessment, though not explicitly contrasting them.

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 provides extensive guidance on when and how to use the tool, including instructions for the AI to extract fields from the card image and pass structured data. It sets guardrails and clarifies what not to do, but does not explicitly state when to avoid this tool in favor of siblings.

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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