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DynamicEndpoints

FantasyPros MCP Server

get_rankings

Retrieve consensus player rankings for NFL or NBA fantasy sports, filterable by position and scoring type to inform draft and lineup decisions.

Instructions

Get consensus rankings for a sport

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYesSport to get rankings for
positionNoPosition to filter by
scoringNoScoring type (for NFL)

Implementation Reference

  • The handler function that implements the core logic of the 'get_rankings' tool by querying the FantasyPros API for consensus rankings.
    private async getRankings(args: any) {
      const { sport, position = 'ALL', scoring = 'STD' } = args;
      const season = new Date().getFullYear().toString();
      const params: any = {
        position,
        scoring,
      };
    
      const response = await this.axiosInstance.get(
        `/${sport}/${season}/consensus-rankings`,
        { params }
      );
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(response.data, null, 2),
          },
        ],
      };
    }
  • src/index.ts:98-121 (registration)
    Registration of the 'get_rankings' tool in the listTools handler, including its metadata and input schema.
    {
      name: 'get_rankings',
      description: 'Get consensus rankings for a sport',
      inputSchema: {
        type: 'object',
        properties: {
          sport: {
            type: 'string',
            enum: ['nfl', 'nba'],
            description: 'Sport to get rankings for',
          },
          position: {
            type: 'string',
            description: 'Position to filter by',
          },
          scoring: {
            type: 'string',
            enum: ['STD', 'PPR', 'HALF'],
            description: 'Scoring type (for NFL)',
          },
        },
        required: ['sport'],
      },
    },
  • src/index.ts:179-180 (registration)
    The switch case in the CallToolRequest handler that dispatches calls to the 'get_rankings' tool to its handler method.
    case 'get_rankings':
      return await this.getRankings(request.params.arguments);
  • The input schema defining parameters for the 'get_rankings' tool.
    inputSchema: {
      type: 'object',
      properties: {
        sport: {
          type: 'string',
          enum: ['nfl', 'nba'],
          description: 'Sport to get rankings for',
        },
        position: {
          type: 'string',
          description: 'Position to filter by',
        },
        scoring: {
          type: 'string',
          enum: ['STD', 'PPR', 'HALF'],
          description: 'Scoring type (for NFL)',
        },
      },
      required: ['sport'],
    },
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't cover critical aspects like whether it's a read-only operation, requires authentication, has rate limits, or what the return format looks like. This leaves significant gaps for a tool that fetches data.

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 directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'consensus rankings' entail, how results are structured, or any behavioral traits like data freshness or limitations. For a data-fetching tool with 3 parameters, this leaves too much unspecified.

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%, so parameters are well-documented in the schema. The description doesn't add any extra meaning beyond the schema, such as explaining how 'consensus rankings' are derived or the significance of 'scoring' types. Baseline 3 is appropriate as 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 verb ('Get') and resource ('consensus rankings for a sport'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'get_players' or 'get_projections' that might also relate to sports data, missing specific distinction.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_players' or 'get_projections'. The description lacks context on use cases, prerequisites, or exclusions, leaving the agent to infer usage based on 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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