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jackdark425

Financial Modeling Prep (FMP) MCP Server

by jackdark425

get_market_gainers

Identify stocks with the largest price increases to analyze market momentum and discover top-performing securities for investment research.

Instructions

Get stocks with the largest price increases (top gainers)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_market_gainers tool is registered and implemented within the registerMarketTools function in src/tools/market.ts. It fetches data from the '/biggest-gainers' endpoint via the fetchFMP utility.
    server.registerTool(
      'get_market_gainers',
      {
        description: 'Get stocks with the largest price increases (top gainers)',
        inputSchema: z.object({}),
      },
      async () => {
        try {
          const data = await fetchFMP<MarketMover[]>('/biggest-gainers');
          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 the full burden of behavioral disclosure. It mentions 'largest price increases' but does not specify timeframes (e.g., daily, weekly), data freshness, rate limits, or authentication needs. This leaves critical behavioral traits undefined for a tool that likely involves real-time or historical market 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 function without unnecessary words. It is front-loaded and wastes no space, making it easy for an agent 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 complexity of market data tools and lack of annotations or output schema, the description is incomplete. It does not explain return values (e.g., format, fields like stock symbols and percentages), data sources, or error handling, which are essential for proper tool invocation in a financial context.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate here, but it could have optionally mentioned implicit parameters like timeframes or limits, though not required. Baseline is 4 for zero parameters.

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: retrieving stocks with the largest price increases, specifically top gainers. It uses a specific verb ('Get') and resource ('stocks'), but does not explicitly differentiate from its sibling 'get_market_losers' or 'get_most_active', which are related but distinct tools for market data.

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 like 'get_market_losers' or 'get_most_active'. It lacks context about prerequisites, such as whether it requires specific market hours or data sources, leaving the agent to infer usage from 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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