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GraphDB MCP Server

by keonchennl
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JavaScript
Remote

GraphDB MCP Server

A Model Context Protocol server that provides read-only access to Ontotext GraphDB. This server enables LLMs to explore RDF graphs and execute SPARQL queries against a GraphDB instance.

Components

Tools

  • sparqlQuery

    • Execute SPARQL queries against the connected GraphDB repository

    • Input:

      • query (string): The SPARQL query to execute

      • graph (string, optional): Specific graph IRI to target

      • format (string, optional): Response format (json, xml, csv)

    • All queries are executed in read-only mode

  • listGraphs

    • Lists all graphs available in the repository

    • No input parameters required

Resources

The server provides multiple views of the repository data:

  • Class List (graphdb://<host>/repository/<repo>/classes)

    • Lists all RDF classes found in the repository with counts

  • Predicates (graphdb://<host>/repository/<repo>/predicates)

    • Lists all predicates (properties) with usage counts

  • Statistics (graphdb://<host>/repository/<repo>/stats)

    • Provides counts of subjects, predicates, objects, and triples

  • Sample Data (graphdb://<host>/repository/<repo>/sample)

    • Shows a sample of triples from the repository

  • Graph Content (graphdb://<host>/repository/<repo>/graph/<graphUri>)

    • Provides sample data from specific graphs along with metadata

Related MCP server: RDF Explorer

Configuration

You can configure the server using environment variables by creating a .env file:

GRAPHDB_ENDPOINT=http://localhost:7200
GRAPHDB_REPOSITORY=myRepository
GRAPHDB_USERNAME=username
GRAPHDB_PASSWORD=password

Alternatively, you can provide the endpoint and repository as command-line arguments:

node dist/index.js http://localhost:7200 myRepository

The command-line arguments take precedence over environment variables.

Usage with Claude Desktop

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:

{
  "mcpServers": {
    "graphdb": {
      "command": "node",
      "args": [
        "/path/to/mcp-server-graphdb/dist/index.js"
      ],
      "env": {
        "GRAPHDB_ENDPOINT": "http://localhost:7200",
        "GRAPHDB_REPOSITORY": "myRepository",
        "GRAPHDB_USERNAME": "username",
        "GRAPHDB_PASSWORD": "password"
      }
    }
  }
}

Replace the values with your specific GraphDB configuration.

Installation

# Clone the repository
git clone https://github.com/keonchennl/mcp-server-graphdb.git
cd mcp-server-graphdb

# Install dependencies
yarn install

# Build the project
yarn build

Example SPARQL Queries

Here are some example SPARQL queries you can run with this server:

  1. List all classes in the ontology:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?class ?label
WHERE {
  { ?class a rdfs:Class } UNION { ?class a owl:Class }
  OPTIONAL { ?class rdfs:label ?label }
}
ORDER BY ?class

  1. List all properties for a specific class:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?property ?label ?range
WHERE {
  ?property rdfs:domain <http://example.org/YourClass> .
  OPTIONAL { ?property rdfs:label ?label }
  OPTIONAL { ?property rdfs:range ?range }
}
ORDER BY ?property

  1. Count instances by class:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?class (COUNT(?instance) AS ?count)
WHERE {
  ?instance a ?class
}
GROUP BY ?class
ORDER BY DESC(?count)

License

This MCP server is licensed under the GPL-3.0 License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the GNU GPL-3.0 License.

Available Tools

2 tools
listGraphsB

List all graphs in the repository

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists graphs but doesn't describe how it behaves—e.g., whether it's read-only, safe, requires authentication, has rate limits, or what the output format might be. This leaves significant gaps in understanding the tool's operational traits.

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 a single, clear sentence with no wasted words. It is front-loaded with the core action and resource, making it highly efficient and easy to understand at a glance.

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

Completeness2/5

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

Given the tool's simplicity (0 parameters, no output schema), the description is minimal but incomplete. It lacks behavioral context, usage guidelines, and output details, which are important even for simple tools. Without annotations or an output schema, the description should do more to inform the agent about what to expect.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't need to add parameter details, as there are none to explain. It efficiently states the tool's purpose without unnecessary parameter information, meeting the baseline for tools with no parameters.

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

Purpose4/5

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

The description clearly states the action ('List') and the resource ('all graphs in the repository'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'sparqlQuery', which might also involve graph operations, so it doesn't reach the highest score of 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus the sibling 'sparqlQuery' or any alternatives. It lacks context about use cases, prerequisites, or exclusions, offering only a basic statement of purpose without operational guidance.

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

sparqlQueryA

Execute a read-only SPARQL query against the GraphDB repository

ParametersJSON Schema
NameRequiredDescriptionDefault
formatNoOptional: Response format (json, xml, csv)json
graphNoOptional: Specific graph IRI to query
queryYesThe SPARQL query to execute

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the 'read-only' behavioral trait, which is crucial for safety, but lacks details on other aspects like authentication needs, rate limits, error handling, or response structure. This is a minimal but adequate disclosure given the simple query nature.

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 a single, front-loaded sentence with zero waste—it directly states the tool's purpose and key constraint ('read-only'). Every word earns its place, making it highly efficient and easy to parse.

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?

Given the tool's moderate complexity (executing queries with parameters) and no annotations or output schema, the description is minimally complete. It covers the core purpose and safety ('read-only') but lacks guidance on result handling or advanced usage, leaving gaps for the agent to infer.

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 description coverage is 100%, so the schema fully documents all three parameters. The description doesn't add any meaning beyond what the schema provides (e.g., no extra details on query syntax or format implications). Baseline 3 is appropriate as the schema does the heavy lifting.

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 clearly states the specific action ('Execute'), resource ('SPARQL query'), and target ('GraphDB repository'), and distinguishes from the sibling tool 'listGraphs' by specifying query execution rather than listing. It's precise and unambiguous.

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 description explicitly states 'read-only', which provides clear context for when to use this tool (for queries that don't modify data). However, it doesn't mention when not to use it or explicitly compare it to the sibling tool 'listGraphs' beyond the implied difference in purpose.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedlistGraphs
    • First observedsparqlQuery
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TDQS

B3.3/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one lists available graphs, while the other executes queries against them. There is no overlap in functionality, making it impossible to confuse their intended use.

Naming Consistency4/5

Both tools use camelCase naming, which is consistent, but they follow different patterns: 'listGraphs' uses a verb-noun structure, while 'sparqlQuery' uses a noun-verb structure. This minor deviation prevents a perfect score.

Tool Count2/5

With only two tools, the server feels thin for a GraphDB domain, lacking essential operations like creating, updating, or deleting graphs or data. This minimal set is insufficient for typical database workflows.

Completeness2/5

The server covers listing and querying but misses critical CRUD operations for a GraphDB system, such as creating graphs, inserting data, or modifying content. This creates significant gaps that will hinder agent tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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