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dylangroos

NHL MCP Server

by dylangroos

get-season-goalie-leaders

Retrieve NHL goalie statistics leaders for a specified season and game type, including categories like wins, save percentage, goals-against average, and shutouts.

Instructions

Get NHL goalie stats leaders for a specific season and game type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesStat category (e.g. wins, savePctg, gaa, shutouts)
seasonYesSeason in YYYYYYYY format (e.g. 20232024)
gameTypeNoGame type (2 for regular season, 3 for playoffs)
limitNoNumber of goalies to return (default: 5)
Behavior2/5

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

With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, what format the data returns, if there are rate limits, authentication requirements, or error conditions. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes all essential scope elements. Every word earns its place without being overly terse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a data retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the leaders data returns (e.g., list of goalies with stats), whether there's pagination, or what happens with invalid parameters. The agent must guess these behavioral aspects, creating uncertainty.

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 fully documents all four parameters. The description mentions 'season and game type' which aligns with two parameters but doesn't add any semantic context beyond what's already in the schema descriptions. This meets the baseline expectation when schema does the heavy lifting.

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 clearly states the action ('Get') and resource ('NHL goalie stats leaders') with specific scope ('for a specific season and game type'), which distinguishes it from general stat leader tools. However, it doesn't explicitly differentiate from its closest sibling 'get-season-stat-leaders' which might include goalies, making it slightly less specific than ideal.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get-current-goalie-leaders' (for current season) or 'get-season-stat-leaders' (which might include skaters), leaving the agent to infer usage context from tool names alone.

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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