Yet Sparql Mcp Server

@yet-market

About 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

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "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"
}

Tools

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Overview

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

FAQ from Yet Sparql Mcp Server

What are the system requirements?

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

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.

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.

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.

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.

Frequently asked questions

What are the system requirements?

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

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.

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.

How does the caching system work?

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.

Comments

Basic information

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