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DynamicEndpoints

FantasyPros MCP Server

get_projections

Retrieve player projections for NFL, MLB, or NBA sports to support fantasy sports decisions. Specify sport, season, and optionally week or position for targeted data.

Instructions

Get player projections for a sport

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYesSport to get projections for
seasonYesSeason year
weekNoWeek number (for NFL)
positionNoPosition to filter by

Implementation Reference

  • Implements the get_projections tool handler by making an API request to FantasyPros for player projections based on sport, season, optional week, and position.
    private async getProjections(args: any) {
      const { sport, season, week, position } = args;
      const params: any = {};
      if (week) params.week = week;
      if (position) params.position = position;
    
      const response = await this.axiosInstance.get(
        `/${sport}/${season}/projections`,
        { params }
      );
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(response.data, null, 2),
          },
        ],
      };
    }
  • Defines the input schema for validating arguments to the get_projections tool.
    inputSchema: {
      type: 'object',
      properties: {
        sport: {
          type: 'string',
          enum: ['nfl', 'mlb', 'nba'],
          description: 'Sport to get projections for',
        },
        season: {
          type: 'string',
          description: 'Season year',
        },
        week: {
          type: 'string',
          description: 'Week number (for NFL)',
        },
        position: {
          type: 'string',
          description: 'Position to filter by',
        },
      },
      required: ['sport', 'season'],
    },
  • src/index.ts:122-148 (registration)
    Registers the get_projections tool in the ListTools response, including name, description, and schema.
    {
      name: 'get_projections',
      description: 'Get player projections for a sport',
      inputSchema: {
        type: 'object',
        properties: {
          sport: {
            type: 'string',
            enum: ['nfl', 'mlb', 'nba'],
            description: 'Sport to get projections for',
          },
          season: {
            type: 'string',
            description: 'Season year',
          },
          week: {
            type: 'string',
            description: 'Week number (for NFL)',
          },
          position: {
            type: 'string',
            description: 'Position to filter by',
          },
        },
        required: ['sport', 'season'],
      },
    },
  • src/index.ts:181-182 (registration)
    Registers the handler dispatch for get_projections in the CallToolRequestHandler switch statement.
    case 'get_projections':
      return await this.getProjections(request.params.arguments);
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 but only states the action without details on permissions, rate limits, data freshness, or response format. It doesn't explain if this is a read-only operation, how projections are generated, or any constraints beyond the input parameters, leaving significant gaps in behavioral context.

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 waste, front-loaded with the core action. It's appropriately sized for a simple tool, avoiding unnecessary elaboration while clearly stating the purpose in a concise manner.

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 tool's complexity (4 parameters, no output schema, no annotations), the description is incomplete. It doesn't cover behavioral aspects like data sources, update frequency, or error handling, and with no output schema, it fails to explain return values. This leaves the agent with insufficient context for effective 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?

The description adds no meaning beyond the input schema, which has 100% coverage with clear descriptions for all 4 parameters (e.g., sport with enum, season, week, position). Since the schema fully documents the parameters, the baseline score is 3, as the description doesn't compensate or provide additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the basic purpose ('Get player projections for a sport') with a clear verb and resource, but it's vague about what 'projections' entail (e.g., statistical forecasts, fantasy points) and doesn't distinguish it from sibling tools like get_players or get_rankings, which might overlap in scope. It provides minimal differentiation beyond the tool name.

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 offers no guidance on when to use this tool versus alternatives like get_players or get_rankings, nor does it specify prerequisites or context for usage. It lacks explicit when/when-not instructions or named alternatives, leaving the agent to infer usage based on the tool name 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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