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dylangroos

NHL MCP Server

by dylangroos

get-team-stats-by-season

Retrieve NHL team statistics for a specific season and game type by providing the team abbreviation and season year.

Instructions

Get statistics for an NHL team for a specific season and game type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamAbbrevYesThree-letter team abbreviation (e.g. TOR, NYR, BOS)
seasonYesSeason in YYYYYYYY format (e.g. 20232024)
gameTypeNoGame type (2 for regular season, 3 for playoffs)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves statistics but does not describe the return format, potential rate limits, error conditions, or data freshness. For a read operation with no annotation coverage, this lack of detail 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 that front-loads the core purpose without unnecessary words. It earns its place by clearly stating what the tool does, making it highly concise and well-structured.

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?

Given the complexity of a data retrieval tool with no annotations and no output schema, the description is incomplete. It does not explain what statistics are returned, their format, or any behavioral traits like pagination or errors. This leaves the agent with insufficient context for reliable use.

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 thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases. Baseline 3 is appropriate when the 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 tool's purpose: 'Get statistics for an NHL team for a specific season and game type.' It specifies the verb ('Get'), resource ('statistics for an NHL team'), and scope ('specific season and game type'). However, it does not explicitly differentiate from sibling tools like 'get-team-stats' or 'get-team-stats-seasons,' which might have overlapping functionality, so it falls short of a perfect score.

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 does not mention prerequisites, exclusions, or comparisons to sibling tools such as 'get-team-stats' or 'get-current-team-stats,' leaving the agent to infer usage from context 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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