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Yet Sparql MCP Server

@yet-market

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

yet-market

提交者

Sidali Temkit

关于 Yet Sparql MCP Server

MCP SPARQL Server is a high-performance, configurable server that connects to any SPARQL endpoint and provides enhanced functionality including result formatting and caching. It's built on top of the MCP (Message Carrying Protocol) framework to provide a seamless, language-agnost

#yet#mcp#sparql

Yet Sparql MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": [
    {
      "name": "sparql",
      "command": "python3",
      "args": [
        "/path/to/server.py",
        "--endpoint",
        "https://data.legilux.public.lu/sparqlendpoint",
        "--format",
        "simplified",
        "--cache-enabled",
        "true",
        "--cache-ttl",
        "300",
        "--cache-strategy",
        "lru"
      ],
      "env": {
        "SPARQL_ENDPOINT": "https://data.legilux.public.lu/sparqlendpoint",
        "SPARQL_TIMEOUT": "30",
        "SPARQL_MAX_RESULTS": "1000",
        "SPARQL_CACHE_ENABLED": "true",
        "SPARQL_CACHE_TTL": "300",
        "SPARQL_CACHE_MAX_SIZE": "100",
        "SPARQL_CACHE_STRATEGY": "lru",
        "PYTHONPATH": "/path/to/project/directory"
      },
      "transport": "stdio"
    }
  ],
  "defaultServer": "sparql"
}

Yet Sparql MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Yet Sparql Mcp Server?

Yet Sparql Mcp Server is a configurable server that connects to any SPARQL endpoint and provides result formatting and caching. It is built on the Message Carrying Protocol (MCP) framework to offer a language-agnostic interface for querying semantic data.

How to use Yet Sparql Mcp Server?

Install via pip install mcp-server-sparql or from source. Start the server by specifying a SPARQL endpoint: mcp-server-sparql --endpoint URL. Queries are sent using the MCP client with JSON actions that include a query_string and optional format parameter. The server can run in foreground mode, as a background daemon, or as a systemd service.

Key features of Yet Sparql Mcp Server

  • Connects to any SPARQL-compliant endpoint
  • Supports SELECT, ASK, CONSTRUCT, and DESCRIBE queries
  • Outputs JSON, simplified JSON, or tabular formats
  • Configurable caching with LRU, LFU, and FIFO strategies
  • Deployable as foreground process, daemon, or systemd service

Use cases of Yet Sparql Mcp Server

  • Querying public or private SPARQL endpoints from MCP‑compatible applications
  • Presenting SPARQL results in a table format for UI components
  • Caching frequently run queries to reduce endpoint load
  • Integrating semantic data workflows into automated pipelines

常见问题

1

What are the system requirements?

Python 3.8 or newer is required, along with the `SPARQLWrapper`, `mcp`, `pydantic`, and `python-daemon` libraries.

2

Can I use any SPARQL endpoint?

Yes, the server is designed to work with any SPARQL-compliant endpoint by specifying its URL via `--endpoint` or the `SPARQL_ENDPOINT` environment variable.

3

What result formats does the server support?

Three formats are available: standard JSON (default), simplified JSON (key-value pairs), and tabular (columns and rows). You can set the format at startup with `--format` or per query with the `format` parameter.

4

How does the caching system work?

The server supports three cache replacement strategies: LRU (least recently used, default), LFU (least frequently used), and FIFO (first in first out). You can configure cache TTL, maximum size, and enable/disable caching via command‑line arguments or environment variables.

5

Can I run the server as a background service?

Yes, use `--daemon` to run as a background process, and optionally set up a systemd service using the provided installation script. Logs and PID files are configurable.

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