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

> ## Agent Instructions
> Phaseo Gateway base URL: https://api.phaseo.app/v1. Send `Authorization: Bearer $PHASEO_API_KEY` with every request.
> Model IDs look like `provider/model`, for example `openai/gpt-6-astra`. Do not guess IDs: list them with `GET /v1/models` or check https://phaseo.app/models. `phaseo/free` routes to an available free model and needs no wallet balance.
> For new text integrations prefer `POST /v1/responses`. `POST /v1/chat/completions` (OpenAI format) and `POST /v1/messages` (Anthropic format) suit existing code.
> The full API contract is https://phaseo.app/docs/openapi/v1/openapi.yaml. Official SDKs: `@phaseo/sdk` (TypeScript) and `phaseo` (Python), plus Go, C#, PHP, Ruby and Rust.
> Search these docs over MCP at https://phaseo.app/docs/mcp, and install Phaseo skills with `npx skills add https://phaseo.app/docs`. If a page is wrong or out of date, report it with the MCP `submit_feedback` tool.
> Manage Phaseo accounts with the `phaseo` CLI (`npm install -g @phaseo/cli`, then `phaseo login` or `phaseo login --device-code`), the `/v1` Management API or the MCP server at `https://mcp.phaseo.app/mcp?access=manage`. Do not send users to the dashboard for account tasks. A person is still needed for sign-up, approving a sign-in, paying the first top-up on the checkout link that `phaseo credits add` returns when no card is saved, confirming each saved-card top-up from the emailed link, and account security (password, MFA, passkeys). Create API keys with `phaseo keys create --name <name> --json`; never guess one. Credit top-ups need `phaseo login --scopes billing:write` first. Task map: https://phaseo.app/docs/v1/developers/manage-from-an-agent.md.

# Tool Calling Patterns

> Reliable tool-calling loops for chat completions and responses endpoints.

Use these patterns to keep tool-calling deterministic and debuggable.

## Pattern 1: Chat Completions loop

1. Send user message + tool definitions.
2. If `finish_reason` is `tool_calls`, execute each tool call.
3. Send tool outputs back as `role: "tool"` messages.
4. Read final assistant answer.

### Request (continuation)

```json theme={null}
{
  "model": "openai/gpt-5-nano",
  "messages": [
    { "role": "user", "content": "What is the weather in Seattle?" },
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [
        {
          "id": "call_123",
          "type": "function",
          "function": {
            "name": "get_weather",
            "arguments": "{\"city\":\"Seattle\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "tool_call_id": "call_123",
      "content": "72F and sunny"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "parameters": {
          "type": "object",
          "properties": { "city": { "type": "string" } },
          "required": ["city"]
        }
      }
    }
  ]
}
```

## Pattern 2: Responses loop

For `/v1/responses`, model tool calls appear as output items of type `function_call`.
Then send a follow-up request with `function_call_output` input items.

### Request (follow-up)

```json theme={null}
{
  "model": "openai/gpt-5-nano",
  "input": [
    {
      "type": "message",
      "role": "user",
      "content": [{ "type": "input_text", "text": "What's the weather in Seattle?" }]
    },
    {
      "type": "function_call",
      "call_id": "call_123",
      "name": "get_weather",
      "arguments": "{\"city\":\"Seattle\"}"
    },
    {
      "type": "function_call_output",
      "call_id": "call_123",
      "output": "72F and sunny"
    }
  ]
}
```

## Endpoint selection

* Prefer `/v1/chat/completions` if you already use OpenAI-style message loops.
* Prefer `/v1/responses` if your app is standardized on the Responses API.
* Use `/v1/messages` if your app is Anthropic-native.


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