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SportScore

get_match_detail

Retrieve detailed match data including score, status, timeline, and lineups by providing the sport and match slug from SportScore results.

Instructions

Get detailed data for a single match by its slug (e.g. 'manchester-united-vs-liverpool'): score, status, timeline, lineups. Slugs come from get_matches results or match URLs on sportscore.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYesSport to query. One of football, basketball, cricket, tennis.
slugYesMatch slug, e.g. 'manchester-united-vs-liverpool'.

Implementation Reference

  • src/index.js:68-82 (registration)
    Tool registration definition in the TOOLS array: name 'get_match_detail', description, inputSchema (sport + slug), API path '/api/widget/match/', and paramMap that maps args to query params.
    {
      name: "get_match_detail",
      description:
        "Get detailed data for a single match by its slug (e.g. 'manchester-united-vs-liverpool'): score, status, timeline, lineups. Slugs come from get_matches results or match URLs on sportscore.com.",
      inputSchema: {
        type: "object",
        properties: {
          sport: sportSchema,
          slug: { type: "string", description: "Match slug, e.g. 'manchester-united-vs-liverpool'." },
        },
        required: ["sport", "slug"],
      },
      path: "/api/widget/match/",
      paramMap: (args) => ({ sport: args.sport, slug: args.slug }),
    },
  • The sportSchema enum validating the sport parameter (football, basketball, cricket, tennis) used by get_match_detail's input schema.
    const sportSchema = {
      type: "string",
      enum: SPORTS,
      description: "Sport to query. One of football, basketball, cricket, tennis.",
    };
  • Generic CallToolRequestSchema handler that dispatches all tools. When called with name 'get_match_detail', it resolves the tool definition from TOOL_BY_NAME, validates sport, calls paramMap to build query params, invokes callApi with the tool's path, and returns the JSON envelope.
    server.setRequestHandler(CallToolRequestSchema, async (req) => {
      const { name, arguments: rawArgs } = req.params;
      const tool = TOOL_BY_NAME.get(name);
      if (!tool) {
        return {
          isError: true,
          content: [{ type: "text", text: `Unknown tool: ${name}` }],
        };
      }
      const args = rawArgs ?? {};
      if (args.sport && !SPORTS.includes(args.sport)) {
        return {
          isError: true,
          content: [
            { type: "text", text: `Invalid sport '${args.sport}'. Must be one of: ${SPORTS.join(", ")}.` },
          ],
        };
      }
    
      const params = tool.paramMap(args);
      let result;
      try {
        result = await callApi(tool.path, params);
      } catch (err) {
        return {
          isError: true,
          content: [{ type: "text", text: `Network error calling SportScore API: ${err.message}` }],
        };
      }
    
      const envelope = {
        tool: name,
        request_url: result.url,
        http_status: result.status,
        data: result.body,
        ...attributionFooter(),
      };
    
      return {
        content: [{ type: "text", text: JSON.stringify(envelope, null, 2) }],
        isError: result.status >= 400,
      };
    });
Behavior3/5

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

With no annotations, the description carries the burden. It discloses the returned data fields but does not mention any behavioral aspects like rate limits, authentication, or error conditions. The read-only nature is implicit.

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?

Two sentences with no redundancy. The first sentence states purpose and key return fields, the second provides parameter context. Every sentence earns its place.

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

Completeness4/5

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

Given no output schema, the description adequately lists the return fields (score, status, timeline, lineups). It is sufficient for a simple retrieval tool, though it could mention potential errors or pagination.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the source of the 'slug' parameter (from get_matches or sportscore URLs), which enhances understanding beyond the schema definition.

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

Purpose5/5

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

The description clearly specifies the verb 'get detailed data' and the resource 'single match by its slug', and distinguishes from sibling get_matches which returns a list. It also lists specific fields (score, status, timeline, lineups).

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

Usage Guidelines4/5

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

It explains that slugs come from get_matches results or match URLs, providing clear context for when to use this tool. It does not explicitly state when not to use it, but the purpose is sufficiently clear.

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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