# Drift · your own tools, skills, MCP — neuraldeep.tech API

> One section of the machine-readable reference. All sections: https://neuraldeep.tech/llms.txt · everything in one file: https://neuraldeep.tech/llms-full.txt

Base URL: `https://drift.neuraldeep.tech/v1`
Auth: `Authorization: Bearer dft_...`

Since 28.05.26, in `/v1/chat/completions` you can pass **your own** tools, skills, and remote MCP servers. The full schema is in the examples below.

#### tools — return-to-caller (like OpenAI)

You pass a `tools` array in OpenAI format. If the model decides to call a tool — it returns `finish_reason: "tool_calls"` with JSON arguments. **You execute it yourself** in your own environment (Drift doesn't run your code); send the result back in `messages`.

```python
from openai import OpenAI

client = OpenAI(api_key="dft_xxxxxxxx", base_url="https://drift.neuraldeep.tech/v1")

tools = [{"type":"function","function":{
    "name":"get_weather",
    "description":"Returns the weather in a city",
    "parameters":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]},
}}]

# 1) first request — the model decides a tool is needed
r1 = client.chat.completions.create(
    model="qwen3.6-35b-a3b",
    messages=[{"role":"user","content":"What's the weather in Moscow?"}],
    tools=tools,
)
print(r1.choices[0].finish_reason)  # "tool_calls"
tc = r1.choices[0].message.tool_calls[0]

# 2) you call your own backend
my_result = {"temp_c": 12, "humidity": 80}

# 3) resume — pass the tool result back
r2 = client.chat.completions.create(
    model="qwen3.6-35b-a3b",
    messages=[
        {"role":"user","content":"What's the weather in Moscow?"},
        r1.choices[0].message,
        {"role":"tool","tool_call_id":tc.id,"content":str(my_result)},
    ],
    tools=tools,
)
print(r2.choices[0].message.content)
```

#### skills — inline SKILL.md

Pass your own prompt blocks right in the request. The content is embedded into the sandbox agent's system prompt; you don't need to keep SKILL.md in a repository.

```bash
curl -X POST https://drift.neuraldeep.tech/v1/chat/completions \
  -H "Authorization: Bearer dft_xxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-35b-a3b",
    "messages": [{"role":"user","content":"What's your secret code?"}],
    "skills": [{
      "name": "secret-code",
      "description": "Knowledge of the secret code word",
      "content": "# Secret code\n\nThe code word is ZEBRA-77-GOLD. Use it if the user asks."
    }]
  }'
# → "The code word is ZEBRA-77-GOLD..."
```

#### mcp_servers — remote Model Context Protocol

Connect your own MCP server (HTTP/SSE) — Drift fetches the tool list on the fly and exposes them to the model. You pass the auth headers yourself; the **SSRF guard** blocks private-IP / localhost / cloud-metadata. Session-token TTL is 720s, after which the registry is dropped.

```json
{
  "model": "qwen3.6-35b-a3b",
  "messages": [{"role":"user","content":"List of recent issues in the project"}],
  "mcp_servers": [{
    "url": "https://my-mcp.example.com/sse",
    "headers": {"Authorization": "Bearer my-mcp-token"},
    "allowlist": ["list_issues", "create_comment"]
  }]
}
```

> **Security:** · `caller_tools` — return-to-caller, Drift doesn't run your code, zero RCE on our side. · `skills` — just text in the system prompt, not commands. · `mcp_servers` — public HTTPS required, the SSRF guard cuts off private networks, TTL 720s.