Yet Sparql Mcp Server
What is Yet Sparql Mcp Server?
Yet Sparql Mcp Server is a high-performance, configurable server that connects to any SPARQL endpoint, providing enhanced functionality such as result formatting and caching. It is built on the MCP (Message Carrying Protocol) framework, offering a seamless, language-agnostic interface for querying semantic data.
How to use Yet Sparql Mcp Server?
To use the server, start it by specifying a SPARQL endpoint using the command: mcp-server-sparql --endpoint https://dbpedia.org/sparql. You can also run it as a background daemon or configure it with systemd for automatic startup.
Key features of Yet Sparql Mcp Server?
- Universal endpoint support for any SPARQL-compliant endpoint.
- Full SPARQL support for executing any valid SPARQL query (SELECT, ASK, CONSTRUCT, DESCRIBE).
- Intelligent result formatting options: standard JSON, simplified JSON, and tabular format.
- High-performance caching with multiple strategies (LRU, LFU, FIFO) and configurable TTL.
- Flexible deployment options including foreground mode, background daemon, and systemd service.
Use cases of Yet Sparql Mcp Server?
- Querying semantic data from various SPARQL endpoints.
- Formatting results for different application needs (e.g., UI display).
- Caching results to improve performance and reduce load on SPARQL endpoints.
FAQ from Yet Sparql Mcp Server?
-
Can I connect to any SPARQL endpoint?
Yes, the server supports any SPARQL-compliant endpoint. -
What formats can I get results in?
You can receive results in standard JSON, simplified JSON, or tabular format. -
Is caching enabled by default?
Yes, caching is enabled by default, and you can configure its behavior.
Server Config
{
"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"
}Recommend Servers
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