Skip to main content
Glama
aahl

AkTools MCP Server

by aahl

美股关键指标

stock_indicators_us

Fetch key financial report indicators for US stocks by providing a stock symbol. Access essential metrics from financial statements.

Instructions

获取美股市场的股票财务报告关键指标

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes股票代码

Implementation Reference

  • The @mcp.tool decorator registers 'stock_indicators_us' as an MCP tool with title '美股关键指标' and description '获取美股市场的股票财务报告关键指标'.
    @mcp.tool(
        title="美股关键指标",
        description="获取美股市场的股票财务报告关键指标",
    )
    def stock_indicators_us(
        symbol: str = field_symbol,
    ):
        dfs = ak_cache(ak.stock_financial_us_analysis_indicator_em, symbol=symbol, indicator="单季报")
        keys = dfs.to_csv(index=False, float_format="%.3f").strip().split("\n")
        return "\n".join(keys[0:15])
  • The handler function 'stock_indicators_us' accepts a 'symbol' parameter, calls ak_cache() with ak.stock_financial_us_analysis_indicator_em to fetch US stock financial indicators (single quarter), converts the result to CSV format, and returns the first 15 rows.
    def stock_indicators_us(
        symbol: str = field_symbol,
    ):
        dfs = ak_cache(ak.stock_financial_us_analysis_indicator_em, symbol=symbol, indicator="单季报")
        keys = dfs.to_csv(index=False, float_format="%.3f").strip().split("\n")
        return "\n".join(keys[0:15])
  • The input schema for the tool: a single 'symbol' parameter (str) using field_symbol which has description '股票代码'.
    def stock_indicators_us(
        symbol: str = field_symbol,
    ):
  • The 'ak_cache' helper function provides a caching layer around akshare API calls, using both in-memory TTL cache and disk-based cache.
    def ak_cache(fun, *args, **kwargs) -> pd.DataFrame | None:
        key = kwargs.pop("key", None)
        if not key:
            key = f"{fun.__name__}-{args}-{kwargs}"
        ttl1 = kwargs.pop("ttl", 86400)
        ttl2 = kwargs.pop("ttl2", None)
        cache = CacheKey.init(key, ttl1, ttl2)
        all = cache.get()
        if all is None:
            try:
                _LOGGER.info("Request akshare: %s", [key, args, kwargs])
                all = fun(*args, **kwargs)
                cache.set(all)
            except Exception as exc:
                _LOGGER.exception(str(exc))
        return all
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. It is minimal and does not disclose behavioral traits such as data source, response format, or any limitations. This is insufficient for a tool with no annotations.

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

Conciseness4/5

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

The description is a single sentence with no wasted words. It is concise, though it could be slightly more informative without losing conciseness.

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 output schema and simple one-parameter input, the description should at least hint at the output (e.g., 'returns a list of key financial ratios'). It fails to provide any context beyond the basic purpose.

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 coverage is 100% with a description for the only parameter. The description adds no extra meaning beyond the schema. Baseline 3 is appropriate.

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 states the tool's function: obtaining key financial report indicators for US stocks. It differentiates from siblings like stock_indicators_a and stock_indicators_hk by specifying the US market.

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?

No explicit guidance on when to use this tool versus alternatives. The market differentiation is implied by the name and description, but no when-to-use or when-not-to-use instructions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/aahl/mcp-aktools'

If you have feedback or need assistance with the MCP directory API, please join our Discord server