Example: Calling through API
url --request POST "$ENDPOINT_URL" \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
-H "Authorization: Bearer $BEARER_TOKEN" \
-d '{"input": "find Motorola contracts"}' --max-time 60 \
| jq .
{
"content": [
{
"thinking": "The user is asking to find contracts related to a specific organization, \"Motorola\". The `identify_contracts_for_organization` tool is designed for this purpose, taking the organization name as input.",
"type": "thinking"
},
{
"id": "fdfa4d22-334d-44d1-861d-749f436097de",
"input": {
"organization_name": "Motorola"
},
"name": "identify_contracts_for_organization",
"type": "cypher_template_tool_use"
},
{
"output": {
"keys": [
"contract_id",
"agreement_type",
"contract_name",
"effective_date",
"renewal_term",
"expiration_date",
"party_names",
"party_roles",
"party_incorporated_countries"
],
"records": [
{
"agreement_type": "Intellectual Property Agreement",
"contract_id": 18,
"contract_name": "Intellectual Property Agreement",
"effective_date": "2014-04-14",
"expiration_date": "unable to determine",
"party_incorporated_countries": [
"United States",
"United States"
],
"party_names": [
"Motorola Solutions, Inc.",
"Zebra Technologies Corporation"
],
"party_roles": [
"Seller",
"Purchaser"
],
"renewal_term": "unable to determine"
}
],
"summary": {}
},
"tool_use_id": "fdfa4d22-334d-44d1-861d-749f436097de",
"type": "cypher_template_tool_result"
},
{
"thinking": "I have already identified the contracts associated with Motorola using the `identify_contracts_for_organization` tool. The output provides a list of contracts with relevant details, directly answering the original query.",
"type": "thinking"
},
{
"text": "Here are the contracts associated with Motorola:\n\n| contract_id | contract_name | agreement_type | effective_date | expiration_date | party_names | party_roles | party_incorporated_countries |\n|---|---|---|---|---|---|---|---|\n| 18 | Intellectual Property Agreement | Intellectual Property Agreement | 2014-04-14 | unable to determine | Motorola Solutions, Inc., Zebra Technologies Corporation | Seller, Purchaser | United States, United States |",
"type": "text"
}
],
"end_reason": "FINAL_ANSWER_PROVIDED",
"role": "assistant",
"status": "SUCCESS",
"type": "message",
"usage": {
"candidates_token_count": 226,
"prompt_token_count": 7148,
"thoughts_token_count": 301,
"total_token_count": 7675
}
Example: Wrapping in an MCP server and calling through Claude Desktop
Neo4j
Connecting agents to knowledge graphs
The promise of knowledge graphs for AI agents has been clear for some time—better accuracy, transparency in reasoning, and more reliable outputs. But turning that promise into reality has been another story entirely. The complexity of building knowledge graphs, configuring GraphRAG retrieval, and deploying production-ready agents has kept these benefits out of reach for many teams.
Neo4j Aura Agent represents an important first step in changing that. By providing a unified platform that connects agents to knowledge graphs in minutes rather than months, it removes much of the ambiguity that has held teams back. The low-code tool creation simplifies how agents achieve accuracy through vector search, Text2Cypher, and query templates working in concert. The built-in reasoning response and human-readable Cypher queries make explainability straightforward rather than aspirational. And the progression from playground testing to secure API deployment with managed inference eliminates the operational friction that often derails AI projects before they reach production.
This is not the final word on knowledge graph-powered agents, but it is a critical step forward. As organizations continue exploring how to make their AI systems more accurate, explainable, and governable, platforms that reduce complexity while preserving power will be essential. Neo4j Aura Agent points the way toward that future, making sophisticated GraphRAG capabilities accessible to teams ready to move beyond vector search and rigid knowledge management systems.
