UNHCR Chart Generation MCP Server

UNHCR Chart Generation MCP Server

Enables AI agents to generate UNHCR-styled data visualizations including bar, line, pie, and scatter charts with refugee and population data, returning charts as base64-encoded images.

Category
访问服务器

README

UNHCR Chart Generation MCP Server

This MCP (Model Context Protocol) server in smithery.ai provides tools for generating UNHCR charts using the FastAPI chart generation service. It allows AI agents to create various types of charts (bar, line, pie, etc.) with UNHCR data visualization.

This server interacts with the UNHCR Chart Generation API.

Features

  • Generate various types of charts (bar, line, pie, scatter) with UNHCR data
  • Create population trend charts for refugee data
  • Generate comparison charts with multiple datasets
  • Customize chart titles, labels, and styling
  • Return charts as base64-encoded images for easy integration

Connect to MCP Server

To access the server, open your web browser and visit the following URL: https://smithery.ai/server/@rvibek/mcp_unhcrpyplot

Configure the MCP host/client as needed.

smithery badge

API Endpoint

The server generates charts using the following API endpoint:

  • https://unhcrpyplot.rvibek.com.np/plot

The API accepts JSON payloads with the following structure:

{
  "chart_type": "string",
  "title": "string",
  "subtitle": "string",
  "x_label": "string",
  "y_label": "string",
  "data": {
    "labels": ["string"],
    "values": [number]
  }
}

MCP Tools

The server exposes the following tools:

generate_unhcr_graph

Generate a UNHCR chart using the FastAPI chart generation service.

Parameters:

  • chart_type (required): Type of chart to generate (bar, line, pie, scatter, etc.)
  • title (required): Main title of the chart
  • subtitle (required): Subtitle describing the chart content
  • x_label (required): Label for the x-axis
  • y_label (required): Label for the y-axis
  • labels (required): List of labels for the data points (e.g., years, countries)
  • values (required): List of numerical values corresponding to the labels

Returns:

  • Dictionary containing the chart image as base64 and metadata

generate_comparison_chart

Generate a comparison chart with multiple datasets.

Parameters:

  • chart_type (required): Type of chart (bar, line, etc.)
  • title (required): Main title of the chart
  • subtitle (required): Subtitle describing the chart content
  • x_label (required): Label for the x-axis
  • y_label (required): Label for the y-axis
  • datasets (required): List of datasets, each containing 'label', 'labels', and 'values'

Returns:

  • Dictionary containing the chart image as base64 and metadata

generate_population_trend_chart

Generate a population trend chart for UNHCR data.

Parameters:

  • years (required): List of years for the x-axis
  • population_counts (required): List of population counts for each year
  • country_name (optional): Name of the country or region being visualized (default: "Country")
  • chart_type (optional): Type of chart (line, bar, etc.) (default: "line")

Returns:

  • Dictionary containing the chart image as base64 and metadata

Example Usage

Here's an example of how to use the generate_unhcr_graph tool:

# Generate a bar chart showing refugee population trends
result = generate_unhcr_graph(
    chart_type="bar",
    title="Nepali Refugees and Asylum Seekers in Canada (2020-2021)",
    subtitle="UNHCR Population Data",
    x_label="Year",
    y_label="Number of People",
    labels=["2020", "2021"],
    values=[205, 114]
)

Response Format

Successful chart generation returns:

{
  "status": "success",
  "chart_type": "bar",
  "title": "Chart Title",
  "image_base64": "base64_encoded_image_string",
  "image_format": "png",
  "message": "Successfully generated bar chart: Chart Title"
}

License

MIT

Acknowledgments

This project uses the UNHCR Chart Generation API for creating visualizations of UNHCR data.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选