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16Coffee

Yahoo Finance MCP Server

by 16Coffee

get_executive_info

Retrieve detailed executive information or compensation data for companies using Yahoo Finance data. Input a company symbol and specify the type of data needed, such as executive profiles, compensation details, or benchmarks, to access precise insights.

Instructions

获取公司高管信息或薪酬数据。

参数说明: info_type: str executives、compensation 或 benchmark symbol: str 当类型为 executives 或 compensation 时必填

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
info_typeYes
symbolNo

Implementation Reference

  • The main handler function implementing the 'get_executive_info' tool. It retrieves company executive information, compensation, or benchmark data from the Financial Modeling Prep API based on the provided info_type and optional symbol.
    async def get_executive_info(info_type: str, symbol: str = "") -> str:
        """获取公司高管或薪酬相关数据"""
    
        api_key = os.environ.get("FMP_API_KEY")
        if not api_key:
            return "Error: FMP_API_KEY environment variable not set."
    
        base = "https://financialmodelingprep.com/stable"
        endpoint_map = {
            "executives": "key-executives",
            "compensation": "governance-executive-compensation",
            "benchmark": "executive-compensation-benchmark",
        }
        endpoint = endpoint_map.get(info_type.lower())
        if not endpoint:
            return "Error: invalid info type"
    
        params = {"apikey": api_key}
        if info_type in ["executives", "compensation"]:
            if not symbol:
                return "Error: symbol is required for this info type"
            params["symbol"] = symbol
    
        url = f"{base}/{endpoint}"
        try:
            resp = requests.get(url, params=params, timeout=10)
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            return f"Error: getting executive info {info_type} for {symbol}: {e}"
        return json.dumps(data)
  • server.py:446-455 (registration)
    The @fmp_server.tool decorator that registers the get_executive_info function as an MCP tool with the specified name and description, including parameter details.
    @fmp_server.tool(
        name="get_executive_info",
        description="""获取公司高管信息或薪酬数据。
    
    参数说明:
        info_type: str
            executives、compensation 或 benchmark
        symbol: str
            当类型为 executives 或 compensation 时必填""",
    )
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 states the tool retrieves data (implying read-only), but doesn't disclose behavioral traits like authentication needs, rate limits, data freshness, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences, but the structure is slightly awkward with a mix of Chinese and English. The first sentence states the purpose, and the second explains parameters, but it could be more front-loaded and polished for clarity.

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

Completeness3/5

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

Given no annotations, 0% schema coverage, and no output schema, the description provides basic purpose and parameter semantics. However, it lacks details on return values, error handling, or behavioral context, making it incomplete for a tool that likely returns complex executive data.

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?

With 0% schema description coverage, the description compensates well by explaining both parameters: info_type specifies the data type (executives, compensation, benchmark) and symbol indicates when it's required. This adds crucial meaning beyond the bare schema, though it doesn't detail parameter formats or constraints.

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 retrieves executive information or compensation data for companies, specifying the resource (executives/compensation/benchmark) and action (获取/retrieve). It distinguishes from siblings like get_financial_statement or get_stock_info by focusing on executive data, though it doesn't explicitly contrast with them.

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

Usage Guidelines3/5

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

The description implies usage through the info_type parameter options (executives, compensation, benchmark) and notes symbol is required for two types. However, it lacks explicit guidance on when to use this tool versus alternatives like get_stock_info or get_analyst_estimates, and doesn't mention prerequisites or exclusions.

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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