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RDF4J MCP Server

RDF4J MCP Server

by odysa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

sparql_select

Executes SPARQL SELECT queries on RDF data and returns results as JSON for tabular data exploration.

Instructions

Execute a SPARQL SELECT query and return results as JSON. Use this for queries that return tabular data with variable bindings.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "sparql_select"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (applied if query has no LIMIT)
queryYesThe SPARQL SELECT query to execute
repository_idNoRepository ID (uses default if not specified)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It adds that results are JSON and represent tabular data, which is useful, but it does not explicitly state read-only semantics, error behavior, or how parameters like limit affect execution. This is only partial transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the primary action, and every word contributes value. No unnecessary information is present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple but has no output schema, so the description should clarify the return shape. It only says 'JSON' and 'tabular data with variable bindings,' which is somewhat vague. It also omits details about limit/repository behavior, though those are in the schema, making it not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters have descriptions in the schema, so coverage is 100%. The description does not add any parameter-specific detail, relying on the schema, which warrants the baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Execute a SPARQL SELECT query and return results as JSON,' clearly identifying the action and output format. It also distinguishes from sibling tools like sparql_construct and sparql_ask by specifying 'tabular data with variable bindings' and the SELECT query form.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Use this for queries that return tabular data with variable bindings' provides a clear context for when to use this tool. However, it does not explicitly list alternatives or state when not to use it, falling short of full alternative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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