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mcp-ubergraph-query

mcp-ubergraph-query

by twhetzel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

query_ubergraph

Execute custom SPARQL queries against the Ubergraph biomedical ontology endpoint. Use for tailored data retrieval when predefined tools are insufficient.

Instructions

Execute a SPARQL query against the Ubergraph endpoint. Use for custom queries when other tools don't fit your needs.

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": "query_ubergraph"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSPARQL query to execute
timeoutNoQuery timeout in seconds (max: 60)
limitNoMaximum results to return (max: 1000)
formatNoResult formatjson

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose behavioral traits such as whether the query can write, authentication needs, rate limits, or potential side effects. The description assumes read-only behavior but does not confirm it.

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?

Two sentences, directly conveying purpose and usage without unnecessary words. Every sentence adds value.

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?

For a tool with 4 parameters and no output schema, the description covers purpose and usage context but lacks details on return format, error handling, or query constraints. It is adequate but not fully comprehensive.

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?

Schema has 100% description coverage for all 4 parameters, so the baseline is 3. The description does not add any extra meaning beyond what is already in the schema.

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?

Clearly states the tool executes SPARQL queries against the Ubergraph endpoint. The phrase 'when other tools don't fit your needs' distinguishes it from siblings like get_hierarchy, get_term_info, and search_terms.

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

Usage Guidelines5/5

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

Explicitly says to use for custom queries when other tools don't fit, providing clear guidance on when to use this tool versus alternatives.

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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