DataGraph

Model Context Protocol server for accessing the DataGraph API from Claude Desktop, ChatGPT, and other MCP-compatible clients.

Installation

Via npx (recommended)

{
  "mcpServers": {
    "datagraph": {
      "command": "npx",
      "args": ["-y", "datagraph-city-mcp-server"],
      "env": {
        "DATAGRAPH_API_KEY": "dgc_your_api_key_here"
      }
    }
  }
}

Via local install

npm install datagraph-city-mcp-server

{
  "mcpServers": {
    "datagraph": {
      "command": "node",
      "args": ["/absolute/path/to/node_modules/datagraph-city-mcp-server/index.js"],
      "env": {
        "DATAGRAPH_API_KEY": "dgc_your_api_key_here"
      }
    }
  }
}

Get an API key

Sign up at datagraph.city to get your free API key.

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Claude Desktop setup

1. Open Claude Desktop config:
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add the configuration above with your API key
3. Restart Claude Desktop

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Example queries

"What programs address violence prevention in Kansas City?"
"Show me the obstacles to reducing urban loneliness in NYC"
"Which organizations fund violence prevention in Kansas City?"
"What strategy areas do Kansas City nonprofits work in?"
"Show recent building permits in Manhattan"
"What are property sale prices by borough in NYC?"

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Available datasets

Call list_datasets first — it returns exact dataset names and current counts.

Locality	Dataset	Description
kc	kansas-city-violence-prevention	450+ programs, 200+ organizations, violence prevention (GOSR)
unlonely-nyc	unlonely-nyc	7,500+ programs addressing urban loneliness (GOSR)
rust-belt	rust-belt-initiatives	5,300+ civic infrastructure programs (GOSR)
nyc	multiple	NYC subway, building permits, property sales, crime, demographics

NYC dataset details

Dataset	Records	Notes
Subway	445 stations	MTA lines and locations
DOB Permits	856,480	Building permits from DOB NOW (March 2021–present)
Property Sales	53,464	Real estate transactions with prices
Crime	100,000	NYPD complaints with demographics
Demographics	195 neighborhoods	Population statistics

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GOSR Framework

Goal-Obstacles-Solutions-Resources (GOSR) is a participatory problem-structuring method for civic problem-solving. Learn more at gosr.ai.

Layer	Role
Goal	The aspirational future state for a community (singular)
Obstacles	Barriers preventing that future — a diagnosis of why the problem persists
Solutions	Potential strategies to overcome each Obstacle (NOT actual programs)
Resources	Actual operating programs that implement a Solution in practice
Actors	Organizations that run Resources
Funders	Foundations and government agencies that fund Actors or Resources (depending on source specificity)
StrategyArea	Practitioner-defined operational groupings (e.g. Prevention, Intervention) — present in some datasets
Ecosystem	Governance stakeholders (elected officials, planning bodies) that set policy — present in some datasets

Key relationship chain:

(Goal)-[:HAS_OBSTACLE]->(Obstacle)-[:HAS_SOLUTION]->(Solution)
    <-[:IMPLEMENTS]-(Resource)<-[:EXECUTES]-(Actor)<-[:FUNDS]-(Funder)
                    (Resource)<-[:FUNDS]-(Funder)  [when source names a specific program]
(Actor)-[:WORKS_IN]->(StrategyArea)

FUNDS edge targeting:

• (Funder)-[:FUNDS]->(Actor) — when the funding source names only the organization (most sources)
• (Funder)-[:FUNDS]->(Resource) — when the funding source names a specific program (currently COMBAT grants only)
• To find all funding, query both: MATCH (f)-[:FUNDS]->(n) WHERE n:Actor OR n:Resource

Critical distinctions:

• Solutions are theoretical/potential interventions — they describe what could work
• Resources are real, existing programs currently operating
• Obstacles can have child Solutions (leaf nodes) OR child Obstacles (sub-problems), but not both

Counting parent vs. leaf Obstacles:

// Parent obstacles (have sub-obstacles)
MATCH (p:Obstacle)-[:HAS_OBSTACLE]->(c:Obstacle)
WHERE p.dataset = 'your-dataset'
RETURN COUNT(DISTINCT p)

// Leaf obstacles (have solutions)
MATCH (p:Obstacle)-[:HAS_OBSTACLE]->(c:Obstacle)
WHERE p.dataset = 'your-dataset'
RETURN COUNT(c)

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Available tools

Tool	Description
list_datasets	List all available datasets — call this first
get_locality_schema	Get the full graph schema for a locality — call before writing Cypher
query_locality_data	Query data using natural language or raw Cypher
explore_locality_data	Browse available data in a locality
analyze_gosr_dataset	Analyze a GOSR dataset for civic problem-solving
get_server_info	Server version and framework reference
get_usage_stats	Your API usage and quota

query_locality_data parameters

Parameter	Required	Description
query	Yes	Natural language query or raw Cypher
locality	No	Locality code, e.g. kc, nyc (default: nyc)
category	No	Filter by category
limit	No	Max results (default: 10)

Tip: Call get_locality_schema first to understand the graph structure, then write Cypher directly for precise results.

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Development

Test with MCP Inspector

npx @modelcontextprotocol/inspector node index.js

Debug mode

DEBUG=* node index.js

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Troubleshooting

"API key not found" — Ensure DATAGRAPH_API_KEY is set in your MCP server env config.

Claude doesn't see the server — Use absolute paths. Restart Claude Desktop completely after config changes.

Empty query results — Use get_locality_schema first to understand the graph structure, then write Cypher directly via query_locality_data.

"Command failed" — Verify Node.js is installed (node --version), then test manually: node index.js.

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Support

• Documentation: docs.datagraph.city
• Issues: github.com/team-earth/datagraph-city-mcp-server/issues
• Email: [email protected]