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

# Server Tools & MCP Integration

> Execute server-side tools and delegate to remote MCP servers using OpenAI SDK.

The Responses API (`/v1/responses`) supports two categories of tools: standard client-side function declarations and server-side tools orchestrated directly by the gateway via the **Model Context Protocol (MCP)** using the official [OpenAI](https://openai.com/?utm_source=neosantara-docs\&utm_medium=referral) SDK.

## Remote MCP Server Integration

By adding MCP server endpoints to the `tools` array, Neosantara operates as an MCP client and executes tools directly at the gateway level without requiring client-side execution loops.

<CodeGroup>
  ```python Python (OpenAI SDK) icon="python" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  from openai import OpenAI
  import os

  client = OpenAI(
      base_url="https://api.neosantara.xyz/v1",
      api_key=os.environ["NEOSANTARA_API_KEY"]
  )

  # Attach remote MCP servers directly in tools array
  response = client.responses.create(
      model="deepseek-v4.1-flash",
      input="Check priority bug issues in our repository.",
      tools=[
          {
              "type": "mcp",
              "server_label": "github",
              "server_url": "https://mcp.github-service.example.com/mcp",
              "headers": {
                  "Authorization": "Bearer gh_mcp_token_secret"
              },
              "allowed_tools": ["list_issues", "get_issue"]
          }
      ]
  )

  print(response.output_text)
  ```

  ```javascript TypeScript (Node.js) icon="js" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  import OpenAI from "openai";

  const client = new OpenAI({
    baseURL: "https://api.neosantara.xyz/v1",
    apiKey: process.env.NEOSANTARA_API_KEY,
  });

  const response = await client.responses.create({
    model: "deepseek-v4.1-flash",
    input: "Check warehouse inventory stock for SKU-9821.",
    tools: [
      {
        type: "mcp",
        server_label: "inventory",
        server_url: "https://mcp.warehouse.example.com/mcp",
        headers: {
          Authorization: "Bearer warehouse_secret_token",
        },
      },
    ],
  });

  console.log(response.output_text);
  ```
</CodeGroup>

## Client-Side Function Declarations

When you want the model to generate structured arguments for functions executed on your own infrastructure, declare standard JSON function tools.

<CodeGroup>
  ```python Python (OpenAI SDK) icon="python" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  from openai import OpenAI
  import os

  client = OpenAI(
      base_url="https://api.neosantara.xyz/v1",
      api_key=os.environ["NEOSANTARA_API_KEY"]
  )

  # Declare custom functions for model evaluation
  response = client.responses.create(
      model="deepseek-v4.1-flash",
      input="What is the weather in Bandung today?",
      tools=[
          {
              "type": "function",
              "function": {
                  "name": "get_weather",
                  "description": "Fetch current weather by city",
                  "parameters": {
                      "type": "object",
                      "properties": {
                          "city": {"type": "string"}
                      },
                      "required": ["city"]
                  }
              }
          }
      ]
  )

  print(response.output_text)
  ```

  ```javascript TypeScript (Node.js) icon="js" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  import OpenAI from "openai";

  const client = new OpenAI({
    baseURL: "https://api.neosantara.xyz/v1",
    apiKey: process.env.NEOSANTARA_API_KEY,
  });

  const response = await client.responses.create({
    model: "deepseek-v4.1-flash",
    input: "What is the weather in Bandung today?",
    tools: [
      {
        type: "function",
        function: {
          name: "get_weather",
          description: "Fetch current weather by city",
          parameters: {
            type: "object",
            properties: {
              city: { type: "string" },
            },
            required: ["city"],
          },
        },
      },
    ],
  });

  console.log(response.output_text);
  ```
</CodeGroup>

## Why Use Server MCP in Responses API?

In standard Chat Completions, when an AI model requests a tool call, your client application must catch `tool_calls`, execute code locally, and return output back to the gateway.

With Responses API and `type: "mcp"`:

1. **Automated Gateway Execution:** Neosantara connects to your remote MCP server directly.
2. **Zero Client Orchestration:** Your application simply awaits the finished result.
3. **Async Synergy:** Combines cleanly with `background=True` for long-running workflows.

## Next Steps

| Task                     | Guide                                                            |
| :----------------------- | :--------------------------------------------------------------- |
| Full MCP Connector Guide | [MCP Connector](/en/agents/mcp-connector)                        |
| Background Tasks         | [Background Tasks](/en/gateway/responses-api/background-jobs)    |
| Conversation State       | [Conversations & State](/en/gateway/responses-api/conversations) |


## Related topics

- [OpenResponses API](/en/gateway/responses-api.md)
- [Function Calling (Tools)](/en/gateway/chat-completions/tool-calling.md)
- [MCP Connector](/en/agents/mcp-connector.md)