Query Table
Q

Query Table

A financial data crawler tool based on Playwright, supporting multi - website queries and the MCP protocol, providing table data crawling and large language model calling functions.
2 points
5.5K

What is MCP Query Table?

MCP Query Table is a financial data query tool that can automatically obtain table data from multiple financial websites and support interaction with large language models. It uses browser automation technology to simulate user operations without dealing with complex API requests and response parsing.

How to use MCP Query Table?

After installation, call the query function through a Python script, specifying query conditions, data source types, and return result formats. The tool will automatically open the browser, execute the query, and return structured data.

Applicable scenarios

It is suitable for scenarios that require regular acquisition of financial data, such as financial data analysis, investment research, and automated report generation. When a certain data source is unavailable, it can quickly switch to other backup data sources.

Main features

Multi - data source support
Supports multiple financial data sources such as Flush Wencai, TDX Wenxiaoda, and Eastmoney Conditional Stock Selection
AI interaction function
Supports interaction with large language models such as Nano Search, Tencent Yuanbao, and Baidu AI Search
Data format conversion
Query results can be directly converted into formats such as Markdown and CSV
MCP protocol support
Supports the Model Context Protocol and can be integrated into AI workflows
Advantages
High development efficiency without dealing with complex request construction and response parsing
What you see is what you get, directly obtaining the table data displayed on the web page
Flexible data source switching to avoid the problem of a single data source being unavailable
Supports headless mode to improve running speed
Limitations
Slower running speed than direct API calls
Requires maintenance of browser automation scripts, and website redesign may cause it to fail
Some websites require pre - login to use all functions
The return result formats of different data sources may be inconsistent

How to use

Install the tool
Install the mcp_query_table package using pip
Start the browser instance
Ensure that the Chrome browser is installed and running, or configure the headless mode
Execute the query
Call the query function to specify query conditions and data sources
Process the results
Convert the returned DataFrame to the required format

Usage examples

Query ETF data
Query the top 200 ETFs with the best returns from Flush
Query fund yields
Query the top 50 funds with the highest yields from the beginning of the year to the present from TDX
AI Q&A
Perform simple mathematical calculations using Tencent Yuanbao

Frequently Asked Questions

Why is the Chrome browser required?
How to solve the problem of website login requirements?
Why are the query results of different data sources inconsistent?
How to configure the headless mode?

Related resources

Playwright official documentation
Documentation for the browser automation framework
MCP protocol description
Model Context Protocol project
Example code repository
Tool source code and examples

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "mcp_query_table": {
      "timeout": 300,
      "command": "D:\\Users\\Kan\\miniconda3\\envs\\py312\\python.exe",
      "args": [
        "-m",
        "mcp_query_table",
        "--format",
        "markdown",
        "--endpoint",
        "http://127.0.0.1:9222",
        "--executable_path",
        "C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"
      ]
    }
  }
}

{
  "mcpServers": {
    "mcp_query_table": {
      "timeout": 300,
      "url": "http://127.0.0.1:8000/sse"
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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