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ChatData MCP Servers: Unmatched Performance & Scalability - MCP Implementation

ChatData MCP Servers: Unmatched Performance & Scalability

ChatData MCP Servers: Enterprise-grade AI infrastructure with unmatched performance, seamless scalability, and ironclad security. Power demanding workloads confidently. #AIUnleashed

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About ChatData MCP Servers

What is ChatData MCP Servers: Unmatched Performance & Scalability?

ChatData MCP Servers is a service framework built on the Model Control Protocol (MCP), designed to enhance the capabilities of large language models (LLMs). It provides a modular architecture with tool systems, prompt templates, and flexible communication interfaces to streamline complex tasks while maintaining high performance and scalability.

Key Features of ChatData MCP Servers

  • Automated tool/prompt discovery: Dynamically register new functions without manual configuration
  • Dual communication modes: Support standard I/O and Server-Sent Events (SSE) for real-time interaction
  • Extensive tool ecosystem: Pre-built modules for web scraping, data processing, code formatting, and multimedia generation
  • Modular prompt system: Reusable templates optimized for content generation, code reviews, and custom workflows
  • Zero-downtime scalability: Adaptive architecture handles increasing workloads while maintaining performance

ChatData MCP Servers Features

How to Use ChatData MCP Servers

Deployment follows three core steps:

  1. Create virtual environment: uv venv --python 3.12
  2. Install dependencies: pip install -e . or via uv pip
  3. Launch servers:
    • Basic mode: server (stdio-based)
    • Real-time mode: server --transport sse --port 8000

Use Cases of ChatData MCP Servers

  • Automated content generation with contextual awareness
  • Real-time web data integration for dynamic responses
  • Code analysis pipelines combining syntax validation and documentation
  • Multi-step workflows combining translation, summarization, and visualization
  • Custom enterprise solutions through tool extension APIs

ChatData MCP Servers FAQ

FAQ from ChatData MCP Servers

  • Q: How do I add custom functionality?
    A: Create tool files in tools/ following the get_tools() interface specification
  • Q: What guarantees performance?
    A: Asynchronous architecture with load balancing across tool execution pipelines
  • Q: Can I combine multiple tools?
    A: Yes, through sequential API calls or orchestration via custom prompt templates
  • Q: Does it support microservices?
    A: Designed for distributed environments with stateless communication patterns

Content

ChatData MCP 服务器

ChatData MCP 服务器是一个基于 Model Control Protocol (MCP) 的服务应用程序,提供了丰富的工具集和提示系统,用于增强大型语言模型的能力。

项目简介

该项目提供了一个灵活的服务器框架,允许通过MCP协议与大型语言模型进行交互。该服务器可以:

  • 执行各种工具函数,扩展模型的能力
  • 提供预设的提示模板,简化常见任务
  • 支持通过标准输入/输出或SSE方式进行通信
  • 自动发现和注册新添加的工具和提示

核心组件

工具系统 (tools/)

工具模块提供了各种功能扩展,使语言模型能够执行具体任务:

  • 网络工具

    • web_scraper.py - 网页内容抓取工具
    • ip_info.py - IP地址信息查询工具
    • http_client.py - HTTP请求客户端
    • fetch.py - 简单网页获取工具
    • browser_use.py - 基于browser_use获取网络信息
  • 数据处理工具

    • data_converter.py - 数据格式转换工具(JSON/YAML/XML)
    • text_summary.py - 文本摘要生成工具
    • calculator.py - 高级数学计算工具
  • 开发辅助工具

    • code_formatter.py - 代码格式化工具
    • postgres.py - PostgreSQL数据库查询工具
  • 多媒体工具

    • image_gen.py - 图像生成工具
  • 其他实用工具

    • translator.py - 文本翻译工具
    • weather.py - 天气查询工具
    • echo.py - 简单的回显工具

提示系统 (prompts/)

提示模块提供了各种预设的提示模板,用于快速生成高质量的回复:

  • simple.py - 基本提示模板
  • content_generator.py - 内容生成提示
  • code_review.py - 代码审查提示
  • utils.py - 提示工具函数

服务器组件 (server/)

服务器模块处理客户端请求和响应,支持多种通信方式:

  • 标准输入/输出 (stdio) 模式
  • 服务器发送事件 (SSE) 模式

安装与使用

uv venv --python 3.12

安装依赖

pip install -e .

或使用 uv

uv pip install -e .

安装playwright

playwright install

启动服务器

stdio 模式(默认)

server

SSE 模式

server --transport sse --port 8000

开发指南

添加新工具

  1. tools/ 目录中创建新的 Python 文件
  2. 实现一个主函数和一个工具调用函数
  3. 提供 get_tools() 函数返回工具定义

示例:

import mcp.types as types

async def my_function(param1, param2):
    # 实现功能
    return [types.TextContent(type="text", text="结果")]

async def my_tool(name: str, arguments: dict):
    if name != "my_tool":
        raise ValueError(f"Unknown tool: {name}")
    
    # 提取参数
    param1 = arguments.get("param1")
    param2 = arguments.get("param2")
    
    return await my_function(param1, param2)

def get_tools():
    return [
        types.Tool(
            name="my_tool",
            description="工具描述",
            inputSchema={
                "type": "object",
                "required": ["param1"],
                "properties": {
                    "param1": {
                        "type": "string",
                        "description": "参数1描述",
                    },
                    "param2": {
                        "type": "string",
                        "description": "参数2描述",
                    }
                },
            },
        )
    ]

添加新提示

  1. prompts/ 目录中创建新的 Python 文件
  2. 实现提示生成函数
  3. 提供 get_prompts() 函数返回提示定义

示例:

import mcp.types as types
from .utils import create_messages

def my_prompt(context=None, topic=None):
    messages = create_messages(context, topic)
    
    return types.GetPromptResult(
        messages=messages,
        description="提示描述"
    )

def get_prompts():
    return [
        types.Prompt(
            name="my_prompt",
            description="提示描述",
            func=my_prompt,
            args={
                "context": {
                    "type": "string",
                    "description": "上下文信息"
                },
                "topic": {
                    "type": "string",
                    "description": "主题"
                }
            }
        )
    ]

许可证

MIT

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