Google Knowledge Graph

News & Research

Operated by Sourcelane✓ verified

Converts lists of entity names (companies, people, places, products) into structured metadata retrieved from the Google Knowledge Graph. Performs entity matching and canonicalization and returns per-match records containing a Knowledge Graph identifier, canonical name, schema.org type annotations, short and long descriptions (often sourced from Wikipedia/Wikidata), official URLs, image URLs, and relevance/confidence scores. Offers configurable match limits to return a single top match or multiple candidate matches for disambiguation, type-based filtering to constrain results to schema.org types (Person, Organization, Place, etc.), multilingual/localized output, and error-tolerant processing that yields error records for failed lookups.

Verified Sep 28, 3:55 AM

$0.002

per entity

Up to 100 per call. Only pay for results returned; failed calls are refunded.

What people use it for

CRM enrichment: turn company name lists into structured profiles with descriptions, logos, and official URLs
Lead scoring: prioritize or filter entities using relevance/confidence scoring and type annotations
Verification of entity legitimacy: distinguish known entities from misspellings or unknown names before outreach
Content categorisation: tag articles, products, or tickets with schema.org types from authoritative entity matches
Entity disambiguation for ML pipelines: resolve ambiguous strings to canonical Knowledge Graph identifiers for downstream processing
Knowledge base seeding: bootstrap graphs of people, organizations, and places with descriptions and images sourced from authoritative Knowledge Graph data

Trust & reliability

7d uptime trend

Window	Uptime	Success rate	p50	p95
24h	—	—	—	—
7d	—	—	—	—
30d	—	—	—	—

Calling contract

Call it through Sourcelane's gateway or MCP server. We run the connector, apply your agent's spend guardrails, and bill only the results returned.

Call it via Sourcelane

curl -X POST https://api.usesourcelane.com/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"google-knowledge-graph","params":{"limit":5},"maxResults":10}'

Request params

Field	Type	Required	Description
`limit`	integer	optional	Maximum number of matches to return per query. Capped at 100 per call.
`types`	array	optional	Restrict results to these schema.org types (e.g. Person, Organization, Place). Leave empty for any type.
`languages`	array	optional	BCP-47 language codes to return results in (e.g. en, es, fr).

Each result contains

Field	Type	Description
`query`	string	Query
`rank`	integer	Rank
`status`	string	Status
`entityId`	string	Entity Id
`name`	string	Name
`types`	array	Types
`description`	string	Description
`detailedDescription`	string	Detailed Description
`detailedDescriptionUrl`	string	Detailed Description Url
`imageUrl`	null	Image Url
`imageContentUrl`	null	Image Content Url
`url`	string	Url
`resultScore`	number	—
`error`	null	—

Use Google Knowledge Graph from Claude, ChatGPT or Cursor

Pick your tool and connect in under a minute. Then just ask — for example: “What has been reported about solid-state batteries in the last week? Cite sources.”

Connect Claude

Web, desktop and mobile. Paste one URL.

1Copy your personal connector URL
2In Claude open Settings → Connectors → Add custom connector
3Paste the URL and click Add — done

Manual setup (config files, REST, Python) +

Claude Code

Adds the Sourcelane MCP server with your key as a header.

terminal

claude mcp add --transport http sourcelane https://api.usesourcelane.com/mcp \
  --header "Authorization: Bearer sl_live_your_agent_key"

Claude Desktop (config file)

Alternative to the connector URL: add to claude_desktop_config.json, then restart Claude.

claude_desktop_config.json

{
  "mcpServers": {
    "sourcelane": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://api.usesourcelane.com/mcp",
        "--header",
        "Authorization:${AUTH_HEADER}"
      ],
      "env": {
        "AUTH_HEADER": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

Cursor & Windsurf

Add to ~/.cursor/mcp.json (or Windsurf's mcp_config.json).

mcp.json

{
  "mcpServers": {
    "sourcelane": {
      "url": "https://api.usesourcelane.com/mcp",
      "headers": {
        "Authorization": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

VS Code

Save as .vscode/mcp.json. VS Code prompts for your key once.

.vscode/mcp.json

{
  "servers": {
    "sourcelane": {
      "type": "http",
      "url": "https://api.usesourcelane.com/mcp",
      "headers": {
        "Authorization": "Bearer ${input:sourcelane-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "sourcelane-key",
      "description": "Sourcelane agent key",
      "password": true
    }
  ]
}

ChatGPT Custom GPT (Actions)

Create a GPT → Actions → Import from URL, then Authentication: API Key, Bearer.

OpenAPI schema URL

https://usesourcelane.com/openapi.json

REST

One POST. Pass maxResults to cap cost.

curl

curl -X POST https://api.usesourcelane.com/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"google-knowledge-graph","params":{"limit":5},"maxResults":10}'

Python, LangChain, CrewAI, OpenAI Agents SDK…

Wrap the REST call as a tool, or point an MCP client at the endpoint.

python

import requests

res = requests.post(
    "https://api.usesourcelane.com/v1/call",
    headers={"Authorization": "Bearer sl_live_your_agent_key"},
    json={
        "listing": "google-knowledge-graph",
        "params": {"limit":5},
        "maxResults": 10,
    },
    timeout=300,
)
body = res.json()
print(body["receipt"]["chargedMicros"], "micro-USD for", body["receipt"]["results"], "results")
print(body["data"])

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