How do I use mcp-ubergraph-query?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-ubergraph-query get info for MONDO:0005015" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

mcp-ubergraph-query

An MCP server for querying the Ubergraph biomedical ontology SPARQL endpoint.

Ubergraph is a merged knowledge graph of OBO ontologies including MONDO, UBERON, HP, CHEBI, GO, CL, and more. This server exposes four tools that let AI assistants query it naturally.

Tools

Tool	Description
`query_ubergraph`	Execute custom SPARQL SELECT queries
`get_term_info`	Get label, definition, synonyms, and types for an ontology term
`search_terms`	Search terms by label or synonym across ontologies
`get_hierarchy`	Traverse parents, children, ancestors, or descendants

Quick Start

Prerequisites

Python 3.10+
uv

Install

git clone https://github.com/twhetzel/mcp-ubergraph-query
cd mcp-ubergraph-query
uv sync --all-extras

Run the server locally

The server uses stdio (stdin/stdout) for MCP transport. Start it with:

uv run mcp-ubergraph-query

Or:

uv run python -m ubergraph_query.server

Leave this process running; MCP clients (e.g. Claude Desktop, Cursor) connect by spawning this command and talking over stdin/stdout.

Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "ubergraph": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-ubergraph-query",
        "run",
        "mcp-ubergraph-query"
      ]
    }
  }
}

Configuration

Copy .env.example to .env and adjust as needed:

cp .env.example .env

Variable	Default	Description
`UBERGRAPH_ENDPOINT`	`https://ubergraph.apps.renci.org/sparql`	SPARQL endpoint URL
`QUERY_TIMEOUT_DEFAULT`	`30`	Default query timeout (seconds)
`QUERY_LIMIT_MAX`	`1000`	Maximum allowed LIMIT value
`ENABLE_QUERY_CACHE`	`true`	Enable in-memory LRU result cache
`CACHE_TTL_SECONDS`	`3600`	Cache entry lifetime
`LOG_LEVEL`	`INFO`	Logging verbosity

Tool Reference

`query_ubergraph`

Execute a custom SPARQL SELECT query against Ubergraph.

Input:

{
  "query": "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 5",
  "timeout": 30,
  "limit": 100,
  "format": "json"
}

Output:

{
  "results": [{"s": "...", "p": "...", "o": "..."}],
  "query_time_ms": 234,
  "result_count": 5,
  "query_hash": "abc123def456"
}

Safety features: LIMIT is automatically injected if absent; write operations (INSERT, DELETE, DROP, etc.) are rejected; timeout is capped at 60 s.

`get_term_info`

Get comprehensive metadata for an ontology term by CURIE.

Input:

{
  "curie": "MONDO:0005015",
  "include_hierarchy": false
}

Output:

{
  "curie": "MONDO:0005015",
  "iri": "http://purl.obolibrary.org/obo/MONDO_0005015",
  "label": "diabetes mellitus",
  "definition": "A metabolic disorder characterized by...",
  "synonyms": ["DM", "diabetes"],
  "types": ["owl:Class"],
  "in_ontology": "mondo"
}

With include_hierarchy: true, parents and children arrays are added.

`search_terms`

Search ontology terms by label or synonym.

Input:

{
  "text": "diabetes",
  "ontologies": ["MONDO", "HP"],
  "limit": 10,
  "exact_match": false
}

Output:

{
  "matches": [
    {
      "curie": "MONDO:0005015",
      "label": "diabetes mellitus",
      "match_type": "partial",
      "ontology": "mondo",
      "score": 0.6
    }
  ],
  "search_text": "diabetes",
  "total_matches": 1
}

`get_hierarchy`

Traverse hierarchical relationships for a term.

Input:

{
  "curie": "MONDO:0005015",
  "relation": "parents",
  "depth": 1
}

relation values: parents, children, ancestors, descendants

Output:

{
  "curie": "MONDO:0005015",
  "relation": "parents",
  "depth": 1,
  "terms": [
    {"curie": "MONDO:0005066", "label": "metabolic disease", "distance": 1}
  ]
}

Example SPARQL Queries

Get term label and definition

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:  <http://purl.obolibrary.org/obo/>
SELECT ?label ?definition WHERE {
  obo:MONDO_0005015 rdfs:label ?label .
  OPTIONAL { obo:MONDO_0005015 obo:IAO_0000115 ?definition }
}

Search by label substring

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?term ?label WHERE {
  ?term rdfs:label ?label .
  FILTER(CONTAINS(LCASE(?label), "diabetes"))
  FILTER(STRSTARTS(STR(?term), "http://purl.obolibrary.org/obo/MONDO_"))
}
LIMIT 10

Get immediate parents

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:  <http://purl.obolibrary.org/obo/>
SELECT ?parent ?label WHERE {
  obo:MONDO_0005015 rdfs:subClassOf ?parent .
  FILTER(!isBlank(?parent))
  OPTIONAL { ?parent rdfs:label ?label }
}

Get all ancestors (transitive)

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:  <http://purl.obolibrary.org/obo/>
SELECT ?ancestor ?label WHERE {
  obo:MONDO_0005015 rdfs:subClassOf+ ?ancestor .
  FILTER(!isBlank(?ancestor))
  OPTIONAL { ?ancestor rdfs:label ?label }
}
LIMIT 100

Find phenotype terms for a disease (HP + MONDO cross-ontology)

PREFIX rdfs:    <http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:     <http://purl.obolibrary.org/obo/>
PREFIX oboInOwl: <http://www.geneontology.org/formats/oboInOwl#>
SELECT ?phenotype ?label WHERE {
  ?association obo:RO_0002200 obo:MONDO_0005015 ;
               obo:RO_0002200 ?phenotype .
  FILTER(STRSTARTS(STR(?phenotype), "http://purl.obolibrary.org/obo/HP_"))
  OPTIONAL { ?phenotype rdfs:label ?label }
}
LIMIT 20

Testing locally

The project is not on PyPI yet. Install and test from the repo:

# Install with dev dependencies (includes pytest)
uv sync --all-extras

# Run unit tests (no network)
uv run python -m pytest tests/ -v

# Test the MCP server: spawns server, lists tools, calls get_term_info, search_terms, get_hierarchy
uv run python examples/test_mcp_server.py

# Run direct SPARQL/query examples (hits Ubergraph)
uv run python examples/example_usage.py

Manual testing with MCP Inspector:
Run the server with uv run mcp-ubergraph-query, then use MCP Inspector and add a stdio server with command uv, args --directory, <path-to-this-repo>, run, mcp-ubergraph-query.

Development

# Lint
uv run ruff check src/ tests/

Project Structure

mcp-ubergraph-query/
├── src/
│   └── ubergraph_query/
│       ├── __init__.py        # Package metadata
│       ├── server.py          # MCP server + tool implementations
│       ├── sparql_client.py   # Async HTTP SPARQL execution with retries
│       ├── query_builder.py   # SPARQL query construction helpers
│       ├── cache.py           # Thread-safe LRU cache with TTL
│       ├── validators.py      # CURIE validation, query safety checks
│       └── config.py          # Environment-based configuration
├── tests/
│   └── test_queries.py        # Unit tests (no network required)
├── examples/
│   └── example_usage.py       # Live query examples
├── pyproject.toml
├── .env.example
└── README.md

Safety

Read-only: Write operations (INSERT, DELETE, DROP, etc.) are rejected
LIMIT enforcement: Queries without LIMIT get one injected; over-limit values are capped
Timeout cap: Hard maximum of 60 seconds per query
Retry with backoff: Transient 5xx/network errors are retried up to 3 times
Query logging: Every query is logged with a SHA-256 hash for provenance

License

MIT

mcp-ubergraph-query

mcp-ubergraph-query

Tools

Quick Start

Prerequisites

Install

Run the server locally

Configure Claude Desktop

Configuration

Tool Reference

`query_ubergraph`

`get_term_info`

`search_terms`

`get_hierarchy`

Example SPARQL Queries

Get term label and definition

Search by label substring

Get immediate parents

Get all ancestors (transitive)

Find phenotype terms for a disease (HP + MONDO cross-ontology)

Testing locally

Development

Project Structure

Safety

License

Resources

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