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NHL MCP Server

by argotdev

get_team_stats

Retrieve comprehensive NHL team statistics including roster details, season performance metrics, and individual player data for analysis and comparison.

Instructions

Get detailed statistics for a specific NHL team including roster, season performance, and player stats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamAbbrevYesTeam abbreviation (e.g., TOR, NYR, BOS, MTL)
seasonNoSeason in format YYYYYYYY (e.g., 20242025), defaults to current season

Implementation Reference

  • Handler for the 'get_team_stats' tool in the CallToolRequestSchema switch statement. Extracts parameters and calls client.getTeamStats(), returning JSON-formatted stats.
    case 'get_team_stats': {
      const stats = await client.getTeamStats(
        parameters.teamAbbrev as string,
        parameters.season as string | undefined
      );
      return {
        content: [{ type: 'text', text: JSON.stringify(stats, null, 2) }],
      };
    }
  • src/index.ts:70-87 (registration)
    Registration of the 'get_team_stats' tool in the TOOLS array used for ListToolsRequestSchema response.
    {
      name: 'get_team_stats',
      description: 'Get detailed statistics for a specific NHL team including roster, season performance, and player stats.',
      inputSchema: {
        type: 'object',
        properties: {
          teamAbbrev: {
            type: 'string',
            description: 'Team abbreviation (e.g., TOR, NYR, BOS, MTL)',
          },
          season: {
            type: 'string',
            description: 'Season in format YYYYYYYY (e.g., 20242025), defaults to current season',
          },
        },
        required: ['teamAbbrev'],
      },
    },
  • Input schema definition for the get_team_stats tool, specifying required teamAbbrev and optional season.
    inputSchema: {
      type: 'object',
      properties: {
        teamAbbrev: {
          type: 'string',
          description: 'Team abbreviation (e.g., TOR, NYR, BOS, MTL)',
        },
        season: {
          type: 'string',
          description: 'Season in format YYYYYYYY (e.g., 20242025), defaults to current season',
        },
      },
      required: ['teamAbbrev'],
    },
  • Helper function in NHLAPI class that fetches team statistics from the NHL API endpoint.
    async getTeamStats(teamAbbrev: string, season?: string): Promise<any> {
      const seasonStr = season || this.getCurrentSeason();
      return this.fetchJSON(`${NHL_API_BASE}/club-stats/${teamAbbrev}/${seasonStr}/2`);
    }
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, implying a read-only operation, but does not specify whether it requires authentication, has rate limits, returns paginated results, or handles errors. For a tool with zero annotation coverage, this lack of behavioral context is a significant gap, though it at least correctly implies a read operation.

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 a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, making it easy to parse. However, it could be slightly improved by structuring key details more explicitly, but it remains highly concise and well-structured.

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

Completeness3/5

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

Given the tool's moderate complexity (retrieving detailed team statistics), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and data scope but lacks details on behavioral traits, usage guidelines, and output format. It meets the basic requirement but leaves gaps that could hinder an agent's effective use, especially without annotations to fill in missing context.

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?

The schema description coverage is 100%, with clear descriptions for both parameters (teamAbbrev and season). The description adds no additional parameter semantics beyond what the schema provides, such as format details or examples not already covered. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 detailed statistics for a specific NHL team including roster, season performance, and player stats.' It specifies the verb ('Get'), resource ('NHL team'), and scope of data returned ('roster, season performance, and player stats'). However, it does not explicitly differentiate from sibling tools like get_team_streak or get_standings, which might also provide team-related statistics, 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 sibling tools like get_team_streak or get_standings, nor does it specify contexts or exclusions for usage. The agent must infer usage based on the purpose alone, which is insufficient for optimal tool selection.

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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