> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vaanivoice.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Call Details

> Get detailed call information including transcript, entities, and evaluation

## Get Call Details

Get detailed call information including transcript, extracted entities, conversation evaluation, and AI-generated summary.

The `user_id` and `client_name` are automatically resolved from your API key - you only need to provide the `call_id`.

## Parameters

**call\_id** `string` *required*

The unique call ID (e.g., `outbound-1776774443-863aa698`)

## Request Example

```bash cURL theme={null}
# CALL_ID=your_call_id
# API_KEY=your_api_key

curl -H "X-API-Key:$API_KEY" "https://api.vaanivoice.ai/api/call_details/$CALL_ID"
```

```javascript JavaScript theme={null}
const CALL_ID = "outbound-1776774443-863aa698";
const API_KEY = "your_api_key";

const response = await fetch(
  `https://api.vaanivoice.ai/api/call_details/${CALL_ID}`,
  {
    method: "GET",
    headers: {
      "X-API-Key": API_KEY,
    },
  }
);

const data = await response.json();
console.log(data);
```

```python Python theme={null}
import requests

CALL_ID = "outbound-1776774443-863aa698"
API_KEY = "your_api_key"

headers = {
    "X-API-Key": API_KEY
}

response = requests.get(
    f"https://api.vaanivoice.ai/api/call_details/{CALL_ID}",
    headers=headers
)

print(response.json())
```

## Response

### Success Response (200)

```json theme={null}
{
  "transcription": "AGENT: Hi! You've reached our customer service. I am your assistant, how can I help you today?\n\n USER: Hello. What are you offering?\n\n AGENT: Namaste! We provide a platform where you can start any business...",
  "entity": {
    "Call_back": "No",
    "Callback_time": null,
    "issue_raised_unresolved_bool": "NO",
    "issue_type_extraction": "N/A"
  },
  "conversation_eval": {},
  "summary": "The agent opened the call with a friendly greeting and introduced themselves as the customer's assistant. The customer asked what was being offered. The agent explained the platform's business services.",
  "call_eval_tag": "not enough data"
}
```

**transcription** `string`

Full conversation transcript with speaker labels (`AGENT:` / `USER:`)

**entity** `object`

Extracted entities from the call

**Entity Properties**

The fields vary based on your agent's configuration. Common fields include:

**Call\_back** `string`

Whether a callback was requested - `"Yes"` or `"No"`

**Callback\_time** `string | null`

Requested callback time, or `null` if none

**issue\_raised\_unresolved\_bool** `string`

Whether an unresolved issue was raised - `"YES"` or `"NO"`

**issue\_type\_extraction** `string`

Type of issue extracted, or `"N/A"`

**conversation\_eval** `object`

Conversation evaluation metrics (structure varies by agent configuration)

**summary** `string`

AI-generated summary of the call conversation

**call\_eval\_tag** `string | null`

Evaluation tag assigned to the call (e.g., `"vkyc_completed_already"`, `"not enough data"`)

### Not Yet Processed

If the call has not been post-processed yet, the response fields will indicate unavailability:

```json theme={null}
{
  "transcription": "Transcript is not available for further evaluations.",
  "entity": "Transcript is not available for further evaluations.",
  "conversation_eval": "Transcript is not available for further evaluations.",
  "summary": "Transcript is not available for further evaluations.",
  "call_eval_tag": null
}
```

### Validation Error (422)

```json theme={null}
{
  "detail": [
    {
      "loc": ["path", "call_id"],
      "msg": "field required",
      "type": "value_error.missing"
    }
  ]
}
```


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