Use Cases

A company has an MCP server that exposes order management tools. They create an agent in the SentientOne platform that instructs the LLM to use those tools to fetch order details, delivery status, and product information.

You are an Order Management Assistant connected to the
company MCP server. You have access to:
  get_order(order_id, user_id)
  get_delivery_status(order_id)
  get_order_products(order_id)
When asked about an order, extract the OrderId and UserId,
call the appropriate tools, and return a JSON summary with
status, items, prices, delivery estimate, and tracking number.

1. 1

   Create the agent in the platform UI

   Pick Custom (or an Order template if available), paste the system prompt above, set output type to JSON, attach your MCP server, and save.
2. 2

   Call the agent from your application

   bash

   curl -X POST https://api.sentientone.ai/v1/chat \
     -H "Content-Type: application/json" \
     -H "X-Api-Key: sk-so-your_api_key_here" \
     -H "X-Agent-Id: ORDER_AGENT_UUID" \
     -d '{
       "message": "Get full order details including delivery and products for OrderId: ORD-78923 and UserId: USR-4412"
     }'

3. 3

   Receive structured response

   json

   {
     "conversation_id": "conv-uuid-...",
     "message": {
       "id": "msg-uuid-...",
       "conversation_id": "conv-uuid-...",
       "role": "assistant",
       "content": "{\"order_id\":\"ORD-78923\",\"status\":\"shipped\",\"customer\":\"USR-4412\",\"items\":[{\"name\":\"Wireless Headphones\",\"qty\":1,\"price\":89.99},{\"name\":\"USB-C Cable\",\"qty\":2,\"price\":12.99}],\"total\":115.97,\"delivery\":{\"carrier\":\"FedEx\",\"tracking\":\"FX-998877\",\"estimated_delivery\":\"2026-04-07\",\"status\":\"in_transit\"}}",
       "token_count": 198,
       "created_at": "2026-03-28T14:30:00Z"
     },
     "tool_calls": [
       {
         "name": "get_order",
         "input": { "order_id": "ORD-78923", "user_id": "USR-4412" },
         "output": "{\"status\":\"shipped\",\"carrier\":\"FedEx\"}"
       }
     ]
   }

A separate agent focused purely on product catalog queries — using Anthropic's Claude with different MCP tools and a different system prompt. Same caller, different X-Agent-Id.

You are a Product Catalog Assistant. You have access to:
  search_products(query, category?, limit?)
  get_product(product_id)
  get_recommendations(product_id, limit?)
Return comprehensive details including name, price,
availability, key specs, and related products.

curl -X POST https://api.sentientone.ai/v1/chat \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: sk-so-your_api_key_here" \
  -H "X-Agent-Id: PRODUCT_AGENT_UUID" \
  -d '{ "message": "Show me the details for product PRD-2210 and suggest similar items" }'

The recommended layout for companies integrating SentientOne into their stack:

• 1. Create agentsIn the SentientOne platform, configure agents with system prompts, models, and LLM keys for each use case.
• 2. Copy your API keyFrom Settings → API Key (sk-so-…).
• 3. Copy your Agent IDFrom Agents → Agent ID chip.
• 4. Call from your appPOST https://api.sentientone.ai/v1/chat with X-Api-Key and X-Agent-Id headers, body { "message": "…" }.
• 5. Parse the responsemessage.content contains the AI response; tool_calls (if present) shows any MCP tool executions.