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XTQuantAI

XTQuantAI

by dfkai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_history_market_data

Retrieve historical market data for specified stock codes, date ranges, and periods with customizable fields using the XTQuantAI MCP server.

Instructions

获取历史行情数据

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "codes": "<codes>"
    },
    "name": "get_history_market_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
codesYes股票代码列表，用逗号分隔，例如 "000001.SZ,600519.SH"
end_dateNo结束日期，格式为 "YYYYMMDD"，为空表示当前日期
fieldsNo字段列表，用逗号分隔，为空表示所有字段
periodNo周期，例如 "1d", "1m", "5m" 等1d
start_dateNo开始日期，格式为 "YYYYMMDD"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('获取' - get) but doesn't reveal any behavioral traits like whether this is a read-only operation, potential rate limits, authentication needs, or what the output format might be. This is a significant gap for a tool with 5 parameters and no output schema.

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 phrase ('获取历史行情数据'), which is highly concise and front-loaded with the core purpose. There is no wasted text, making it efficient, though it could benefit from slightly more detail to enhance clarity without sacrificing brevity.

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 a 5-parameter tool with no annotations and no output schema, the description is incomplete. It doesn't address key contextual aspects like the return format, error handling, or how it integrates with sibling tools. This leaves gaps that could hinder an AI agent's ability to use the tool effectively.

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?

The schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., 'codes' as stock codes, 'period' as time intervals). The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or default behaviors. Given the high schema coverage, a baseline score of 3 is appropriate.

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

Purpose3/5

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

The description '获取历史行情数据' (Get historical market data) states a clear verb ('获取' - get) and resource ('历史行情数据' - historical market data), establishing the basic purpose. However, it lacks specificity about what type of market data (e.g., stocks, indices) and doesn't differentiate from sibling tools like 'get_full_market_data' or 'get_latest_market_data', making it somewhat vague.

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. There are no explicit instructions, implied contexts, or exclusions mentioned, such as how it differs from 'get_latest_market_data' for real-time data or 'get_full_market_data' for broader data. This leaves the agent without usage direction.

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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