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jackdark425

Financial Modeling Prep (FMP) MCP Server

by jackdark425

get_sector_performance

Retrieve current sector performance data to analyze market trends and identify leading or lagging sectors for investment decisions.

Instructions

Get current sector performance snapshot

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate in YYYY-MM-DD format (optional, defaults to latest)

Implementation Reference

  • The 'get_sector_performance' tool is registered and implemented directly in src/tools/market.ts. It fetches a sector performance snapshot using the fetchFMP utility.
    server.registerTool(
      'get_sector_performance',
      {
        description: 'Get current sector performance snapshot',
        inputSchema: SectorPerformanceSchema,
      },
      async (args: z.infer<typeof SectorPerformanceSchema>) => {
        try {
          const date = args.date || new Date().toISOString().split('T')[0];
          const data = await fetchFMP<SectorPerformance[]>(`/sector-performance-snapshot?date=${date}`);
          return jsonResponse(data);
        } catch (error) {
          return errorResponse(error);
        }
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'current snapshot' which implies real-time or latest data, but doesn't disclose behavioral traits like rate limits, data freshness, authentication needs, or what happens if the date is invalid. For a tool with no annotations, this leaves significant gaps in understanding its operation.

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 no wasted words. It's front-loaded with the core purpose, 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 no annotations and no output schema, the description is too sparse for a data-fetching tool. It doesn't explain what a 'sector performance snapshot' includes (e.g., metrics, format), how results are structured, or error handling. This inadequately prepares an agent 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?

Schema description coverage is 100%, so the schema fully documents the optional date parameter. The description adds no additional parameter semantics beyond implying 'current' data, which aligns with the schema's default behavior. This meets the baseline for high schema coverage.

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 action ('Get') and resource ('current sector performance snapshot'), making the purpose understandable. It doesn't explicitly differentiate from siblings like get_market_gainers or get_sp500_constituents, which also provide market data, but the specific focus on sector performance is reasonably distinct.

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. It doesn't mention prerequisites, timing, or how it differs from other market data tools in the sibling list, leaving the agent to infer usage based on the 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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