Skill v1.0.1

name: neo4j-aura-agent-skill description: Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, or invoking agents with natural language queries. Covers OAuth2 auth, organization/project scoping, tool parameter schemas, and InvokeAgentResponse format. Does NOT cover AuraDB instance provisioning — use neo4j-aura-provisioning-skill. Does NOT cover vector index creation — use neo4j-vector-index-skill. version: 1.0.1 allowed-tools: Bash WebFetch

When to Use

• Creating or configuring an Aura Agent on an existing AuraDB instance
• Adding/updating tools (CypherTemplate, SimilaritySearch, Text2Cypher) to an agent
• Deploying an agent for external access (REST API endpoint or MCP server)
• Invoking an agent with natural language queries via REST API
• Listing, reading, or deleting existing agents in a project

When NOT to Use

• Creating/managing AuraDB instances → neo4j-aura-provisioning-skill
• Creating vector indexes → neo4j-vector-index-skill
• Running Cypher directly → neo4j-cypher-skill
• Building Aura Graph Analytics sessions → neo4j-aura-graph-analytics-skill

What are Aura Agents

GraphRAG agents on top of AuraDB — answer natural language questions via three tool types:

• CypherTemplate — parameterized queries for predictable lookups
• SimilaritySearch — vector similarity search over a VECTOR index
• Text2Cypher — natural language → Cypher for aggregations and discovery

Expose your graph via natural language to users or apps without application code. Accessible as REST or MCP endpoint; single- and multi-turn. For full Cypher control, low-latency lookups, or direct writes — use neo4j-cypher-skill instead.

Prerequisites

• Running AuraDB instance with knowledge graph loaded
• "Generative AI assistance" enabled in Organization settings
• "Aura Agent" toggled on in the project
• "Tool authentication" enabled at project/Security level
• Project admin access
• AURA_CLIENT_ID and AURA_CLIENT_SECRET from console.neo4j.io → Account Settings → API Credentials
• AURA_ORG_ID, AURA_PROJECT_ID — see Step 2; AURA_INSTANCE_ID — resolved interactively in Step 2 if not already set
• Python env: uv sync in skill directory (or pip install neo4j neo4j-graphrag requests python-dotenv)
• .env and schema.json in .gitignore

Step 1 — Verify Auth

Manual credential verification only — scripts call get_token() internally.

TOKEN=$(curl -s --request POST 'https://api.neo4j.io/oauth/token' \
  --user "${AURA_CLIENT_ID}:${AURA_CLIENT_SECRET}" \
  --header 'Content-Type: application/x-www-form-urlencoded' \
  --data-urlencode 'grant_type=client_credentials' \
  | jq -r '.access_token')
echo "Token: ${TOKEN:0:20}..."

If blank token: verify AURA_CLIENT_ID/AURA_CLIENT_SECRET in .env. Stop and report. Token TTL: 3600 s. Re-run on 401/403.

Step 2 — Resolve Organization & Project IDs

From console URL (fastest): open console.neo4j.io → navigate to a project. URL pattern: /organizations/{AURA_ORG_ID}/projects/{AURA_PROJECT_ID}

Programmatic fallback:

curl -s https://api.neo4j.io/v1/tenants \
  -H "Authorization: Bearer $TOKEN" | jq '.data[] | {id, name}'
# tenant id maps to AURA_PROJECT_ID

Set in .env:

AURA_ORG_ID=<organization-id>
AURA_PROJECT_ID=<project-id>

Check `AURA_INSTANCE_ID` — if it is already set in .env, skip the rest of this step.

If not set, list available instances and ask the user to choose:

curl -s "https://api.neo4j.io/v1/instances?tenantId=${AURA_PROJECT_ID}" \
  -H "Authorization: Bearer $TOKEN" \
  | jq '.data[] | {id, name, status, region, type}'

Show output to user. Ask: "Which instance should the agent connect to?" Then write to .env:

AURA_INSTANCE_ID=<chosen-instance-id>
NEO4J_URI=neo4j+s://<chosen-instance-id>.databases.neo4j.io

If the list is empty: no AuraDB instances exist in this project — an Aura Agent cannot be created without one. Stop and report. If 401: re-run Step 1. If 404: verify AURA_PROJECT_ID. Stop and report.

Step 3 — List Existing Agents

uv run python3 scripts/manage_agent.py list   # Linux/macOS
uv run python scripts\manage_agent.py list    # Windows

Output: agent IDs, names, enabled status, endpoint URLs.

If 401: re-run Step 1. If 404: verify AURA_ORG_ID/AURA_PROJECT_ID. Stop and report.

Step 4 — Fetch Graph Schema

Requires NEO4J_URI, NEO4J_USERNAME, NEO4J_PASSWORD in .env.

uv run python3 scripts/fetch_schema.py   # Linux/macOS
uv run python scripts\fetch_schema.py    # Windows

Saves schema.json. Output: node/rel-type counts, node labels + typed properties (with Aura data_type), relationship patterns, VECTOR indexes.

Data gate — script exits with error and does NOT write schema.json if:

• fewer than 2 nodes, OR
• zero relationship types

If gate fails: load data into the database before proceeding. Stop and report. If ServiceUnavailable: check NEO4J_URI uses neo4j+s://; instance must be running. Stop and report. If neo4j-graphrag not found: uv add neo4j-graphrag. Stop and report.

Read schema.json before Step 5.

Step 5 — Discover Use Cases

Before designing tools, read references/authoring-guide.md.

Ask the user these questions. Do NOT guess tool types or parameters.

1. "What questions should this agent answer?"
2. "Which nodes or relationships matter most?" — match against schema.json → node_props
3. "Do users search by a specific property value?" → CypherTemplate
4. "Any counting, grouping, or date-range questions?" → Text2Cypher
5. "Search for semantically similar text?" → check schema.json → metadata → vector_index

• No VECTOR index found: inform user; skip SimilaritySearch; delegate to neo4j-vector-index-skill first
• VECTOR index found: ask the user — "Which embedding provider and model should be used? What output dimension?" See supported models in references/REFERENCE.md → Embedding Provider Options. Do NOT guess or default.

Tool selection:

CypherTemplate parameters: for each parameter, read aura_data_type from schema.json → node_props or rel_props and use it as data_type. If the property has low_cardinality: true, the parameter description MUST list the valid values — copy them from the values array in schema.json. Example: "description": "Agreement type to filter by. Valid values: \"Distributor Agreement\", \"License Agreement\", \"NDA\"". Properties with has_fulltext_index: true are especially likely to be filter targets and must include valid values when low cardinality.

SimilaritySearch configuration — ask the user for all three before drafting the tool config:

index: use name from schema.json → metadata → vector_index where state = ONLINE. dimension must match vector.dimensions in the same index entry.

Signals inventory: for each label or relationship that appears in a tool or the user's stated questions, write a signal block in the system prompt. See references/authoring-guide.md → Signals inventory for the template and rules.

Draft config JSON → show to user for review → confirm → proceed to Step 6.

Step 6 — Create Agent

Minimum required config:

{
  "name": "My Agent",
  "description": "Answers questions about the graph",
  "dbid": "<AURA_INSTANCE_ID>",
  "is_private": false,
  "tools": [
    {
      "type": "text2cypher",
      "name": "Query Graph",
      "description": "Translates natural language questions into Cypher queries"
    }
  ]
}

Show config to user and confirm before running:

uv run python3 scripts/manage_agent.py create --config agent-config.json

Response includes id (save as AURA_AGENT_ID), endpoint_link, mcp_endpoint_link.

Step 7 — Invoke Agent (Test)

uv run python3 scripts/invoke_agent.py --agent-id "$AURA_AGENT_ID" "What can you help me with?"

--raw prints full JSON including reasoning chain and token usage.

Direct curl (uses token from Step 1):

curl -s -X POST \
  "https://api.neo4j.io/v2beta1/organizations/${AURA_ORG_ID}/projects/${AURA_PROJECT_ID}/agents/${AURA_AGENT_ID}/invoke" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"input": "What can you help me with?"}'

Step 8 — Update Agent (Partial PATCH)

Create patch JSON with only the fields to change:

{ "system_prompt": "Updated instructions.", "is_mcp_enabled": true }

Show to user and confirm before running:

uv run python3 scripts/manage_agent.py update --agent-id "$AURA_AGENT_ID" --config patch.json

Step 9 — Delete Agent

IRREVERSIBLE. Configuration permanently removed.

Show to user and wait for explicit confirmation before running:

uv run python3 scripts/manage_agent.py delete --agent-id "$AURA_AGENT_ID"

Returns 202 Accepted.

Tool Configuration

CypherTemplate

Pre-defined parameterized queries for repeated, predictable lookups.

{
  "type": "cypherTemplate",
  "name": "<descriptive name>",
  "description": "<what it looks up and when to use it>",
  "enabled": true,
  "config": {
    "template": "MATCH (n:Label {prop: $param}) RETURN n",
    "parameters": [
      {
        "name": "param",
        "data_type": "<string|integer|number|boolean — from schema.json aura_data_type>",
        "description": "<what the parameter represents. If low_cardinality=true in schema.json, append: Valid values: \"val1\", \"val2\", ...>"
      }
    ]
  }
}

Low-cardinality rule: if schema.json → node_props[Label][prop].low_cardinality is true, the description field must end with the exact values from schema.json → node_props[Label][prop].values. This applies to relationship properties in rel_props too.

SimilaritySearch

Requires a VECTOR index (state = ONLINE). Get index name from schema.json → metadata → vector_index.

{
  "type": "similaritySearch",
  "name": "<descriptive name>",
  "description": "<what text it searches and when to use it>",
  "enabled": true,
  "config": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "index": "<name from schema.json metadata.vector_index[state=ONLINE].name>",
    "top_k": 5,
    "dimension": "<vector.dimensions from schema.json metadata.vector_index options.indexConfig>",
    "post_processing_cypher": "<optional: Cypher to enrich similarity results with related nodes>"
  }
}

provider/model combinations: see references/REFERENCE.md.

Text2Cypher

Natural language → Cypher. Use as fallback for aggregation and discovery.

{
  "type": "text2cypher",
  "name": "<descriptive name>",
  "description": "<what questions it handles — and explicitly what it should NOT handle>",
  "enabled": true
}

Common Errors

Scripts

All scripts load credentials from .env automatically. Run with uv run python3 <script>.

fetch_schema.py parameters:

manage_agent.py parameters:

invoke_agent.py parameters:

Checklist

• [ ] AuraDB instance running, knowledge graph loaded
• [ ] "Generative AI assistance" + "Aura Agent" enabled in org/project settings
• [ ] .env populated: AURA_CLIENT_ID, AURA_CLIENT_SECRET, AURA_ORG_ID, AURA_PROJECT_ID, AURA_INSTANCE_ID, NEO4J_URI, NEO4J_PASSWORD
• [ ] .env and schema.json in .gitignore
• [ ] Auth verified (Step 1)
• [ ] Org/Project IDs confirmed (Step 2)
• [ ] API connectivity confirmed via list (Step 3)
• [ ] schema.json fetched and reviewed (Step 4) — data gate passed (≥2 nodes, ≥1 rel type)
• [ ] Use cases confirmed with user (Step 5)
• [ ] CypherTemplate data_type taken from schema.json aura_data_type
• [ ] SimilaritySearch index from schema.json metadata.vector_index (state=ONLINE)
• [ ] Agent config shown to user and confirmed (Step 6)
• [ ] Required fields present: name, description, dbid, is_private, tools (min 1)
• [ ] AURA_AGENT_ID saved from create response
• [ ] Agent invoked and response verified (Step 7)
• [ ] Update/Delete confirmed by user before execution