curl --request POST \
  --url https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "text": "Hello, please introduce yourself"
        }
      ]
    }
  ]
}
'

import requests

url = "https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent"

payload = { "contents": [
        {
            "role": "user",
            "parts": [{ "text": "Hello, please introduce yourself" }]
        }
    ] }
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    contents: [{role: 'user', parts: [{text: 'Hello, please introduce yourself'}]}]
  })
};

fetch('https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'contents' => [
        [
                'role' => 'user',
                'parts' => [
                                [
                                                                'text' => 'Hello, please introduce yourself'
                                ]
                ]
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent"

	payload := strings.NewReader("{\n  \"contents\": [\n    {\n      \"role\": \"user\",\n      \"parts\": [\n        {\n          \"text\": \"Hello, please introduce yourself\"\n        }\n      ]\n    }\n  ]\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"contents\": [\n    {\n      \"role\": \"user\",\n      \"parts\": [\n        {\n          \"text\": \"Hello, please introduce yourself\"\n        }\n      ]\n    }\n  ]\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://direct.evolink.ai/v1beta/models/gemini-3.1-pro-preview-customtools:generateContent")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"contents\": [\n    {\n      \"role\": \"user\",\n      \"parts\": [\n        {\n          \"text\": \"Hello, please introduce yourself\"\n        }\n      ]\n    }\n  ]\n}"

response = http.request(request)
puts response.read_body

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "Hello! I'm glad to introduce myself.\n\nI'm a large language model trained and developed by Google..."
          }
        ]
      },
      "finishReason": "STOP",
      "index": 0,
      "safetyRatings": [
        {}
      ]
    }
  ],
  "promptFeedback": {
    "safetyRatings": [
      {}
    ]
  },
  "usageMetadata": {
    "promptTokenCount": 4,
    "candidatesTokenCount": 611,
    "totalTokenCount": 2422,
    "thoughtsTokenCount": 1807,
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 4
      }
    ]
  }
}

{
  "error": {
    "code": 400,
    "message": "Paramètres de requête invalides",
    "type": "invalid_request_error"
  }
}

{
  "error": {
    "code": 401,
    "message": "Invalid or expired token",
    "type": "authentication_error"
  }
}

{
  "error": {
    "code": 402,
    "message": "Quota insuffisant",
    "type": "insufficient_quota_error",
    "fallback_suggestion": "https://evolink.ai/dashboard/billing"
  }
}

{
  "error": {
    "code": 403,
    "message": "Access denied for this model",
    "type": "permission_error"
  }
}

{
  "error": {
    "code": 404,
    "message": "Model not found",
    "type": "not_found_error"
  }
}

{
  "error": {
    "code": 429,
    "message": "Limite de débit dépassée",
    "type": "rate_limit_error",
    "fallback_suggestion": "retry after 60 seconds"
  }
}

{
  "error": {
    "code": 500,
    "message": "Erreur interne du serveur",
    "type": "internal_server_error",
    "fallback_suggestion": "try again later"
  }
}

{
  "error": {
    "code": 502,
    "message": "Upstream AI service unavailable",
    "type": "upstream_error",
    "fallback_suggestion": "try again later"
  }
}

{
  "error": {
    "code": 503,
    "message": "Service temporairement indisponible",
    "type": "service_unavailable_error",
    "fallback_suggestion": "retry after 30 seconds"
  }
}