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Refactor to use LiteLLM Responses API for automatic MCP tool execution

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Major refactoring to properly integrate with LiteLLM's Responses API, which handles
MCP tool execution automatically instead of requiring manual tool call loops.

Key changes:
- Switched from chat.completions.create() to client.responses.create()
- Use "server_url": "litellm_proxy" to leverage LiteLLM as MCP gateway
- Set "require_approval": "never" for fully automatic tool execution
- Simplified get_available_mcp_tools() to get_available_mcp_servers()
- Removed manual OpenAI tool format conversion (LiteLLM handles this)
- Updated response extraction to use output[0].content[0].text format
- Convert system prompts to user role for Responses API compatibility

Technical improvements:
- LiteLLM now handles the complete tool calling loop automatically
- No more placeholder responses - actual MCP tools will execute
- Cleaner code with ~100 fewer lines
- Better separation between tools-enabled and tools-disabled paths
- Proper error handling for Responses API format

Responses API benefits:
- Single API call returns final response with tool results integrated
- Automatic tool discovery, execution, and result formatting
- No manual tracking of tool_call_ids or conversation state
- Native MCP support via server_label configuration

Documentation:
- Added comprehensive litellm-mcp-research.md with API examples
- Documented Responses API vs chat.completions differences
- Included Discord bot migration patterns
- Covered authentication, streaming, and tool restrictions

Next steps:
- Test with actual Discord interactions
- Verify GitHub MCP tools execute correctly
- Monitor response extraction for edge cases

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Josh Knapp
2025-12-12 10:32:04 -08:00
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# LiteLLM Responses API with MCP tool integration
LiteLLM's `/v1/responses` endpoint enables automatic MCP tool execution through a single API call, eliminating the manual tool-calling loop required with chat.completions. When configured with `"require_approval": "never"`, LiteLLM handles tool discovery, execution, and response integration automatically—making Discord bot migration straightforward. The key differences from chat.completions are the `input` parameter (replacing `messages`) and native MCP tool support via a `"type": "mcp"` tool specification.
## Request and response format for /v1/responses
The Responses API (available in LiteLLM **1.63.8+**) uses `input` instead of `messages`. The `input` parameter accepts either a simple string or an array of message objects:
```python
# Simple string input
response = client.responses.create(
model="anthropic/claude-3-5-sonnet-latest",
input="What is the weather today?"
)
# Array format (for multi-turn conversations)
response = client.responses.create(
model="anthropic/claude-3-5-sonnet-latest",
input=[
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
{"role": "user", "content": "Tell me about Python"}
]
)
```
**Response structure** differs significantly from chat.completions. Instead of `choices[0].message.content`, responses use an `output` array:
```json
{
"id": "resp_abc123",
"object": "response",
"created_at": 1734366691,
"status": "completed",
"model": "claude-3-5-sonnet-latest",
"output": [
{
"type": "message",
"id": "msg_abc123",
"status": "completed",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Here is the response text...",
"annotations": []
}
]
}
],
"usage": {"input_tokens": 18, "output_tokens": 98, "total_tokens": 116}
}
```
To extract text: `response.output[0].content[0].text`
## MCP tool specification format
MCP tools use `"type": "mcp"` with three critical parameters: `server_label`, `server_url`, and `require_approval`. The special value `"server_url": "litellm_proxy"` tells LiteLLM to act as an MCP gateway, handling all tool execution internally:
```python
tools=[
{
"type": "mcp",
"server_label": "my_mcp_server", # Identifier for the MCP server
"server_url": "litellm_proxy", # LiteLLM handles MCP bridging
"require_approval": "never", # Automatic execution
"allowed_tools": ["tool1", "tool2"] # Optional: restrict available tools
}
]
```
| Parameter | Purpose |
|-----------|---------|
| `server_label` | Identifies which configured MCP server to use (must match config.yaml) |
| `server_url` | `"litellm_proxy"` for LiteLLM gateway, or direct URL like `"https://mcp.example.com/mcp"` |
| `require_approval` | `"never"` for automatic execution; omit for approval-based flow |
| `allowed_tools` | Whitelist of tool names to make available |
When `server_url="litellm_proxy"`, LiteLLM performs a **four-step automatic flow**: (1) fetches MCP tools and converts to OpenAI format, (2) sends tools to the LLM with your input, (3) executes any tool calls against MCP servers, and (4) returns the final response with tool results integrated.
## Streaming versus non-streaming responses
For **non-streaming**, pass `stream=False` (default) and receive the complete response object:
```python
response = client.responses.create(
model="gpt-4o",
input="Hello",
stream=False
)
text = response.output[0].content[0].text
```
For **streaming**, set `stream=True` and iterate over events:
```python
stream = client.responses.create(
model="gpt-4o",
input="Write a poem",
stream=True
)
full_text = ""
for event in stream:
if hasattr(event, 'type'):
if event.type == "response.output_text.delta":
print(event.delta, end="", flush=True)
full_text += event.delta
elif event.type == "response.completed":
print("\n--- Done ---")
```
Key streaming event types include `response.created`, `response.output_text.delta` (incremental text), `response.output_text.done`, and `response.completed`.
## Python SDK differences between responses.create() and chat.completions.create()
| Aspect | `responses.create()` | `chat.completions.create()` |
|--------|---------------------|---------------------------|
| Input parameter | `input` (string or array) | `messages` (array required) |
| Response access | `response.output[0].content[0].text` | `response.choices[0].message.content` |
| Conversation history | Built-in via `previous_response_id` | Manual message array management |
| MCP tools | Native `"type": "mcp"` support | Standard function calling only |
| Endpoint | `/v1/responses` | `/v1/chat/completions` |
**Client setup** is identical for both APIs:
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:4000", # Your LiteLLM proxy
api_key="sk-your-litellm-key"
)
# Responses API
response = client.responses.create(model="gpt-4o", input="Hello")
# Chat Completions API (old way)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
```
## Conversation history with the input parameter
Unlike chat.completions where you manually pass the full message history each time, the Responses API offers two approaches:
**Option 1: Use `previous_response_id`** for automatic context (recommended):
```python
# First message
response1 = client.responses.create(model="gpt-4o", input="My name is Alice")
# Follow-up with context preserved automatically
response2 = client.responses.create(
model="gpt-4o",
input="What's my name?",
previous_response_id=response1.id # LiteLLM maintains context
)
```
**Option 2: Pass full history in input array** (manual approach):
```python
response = client.responses.create(
model="gpt-4o",
input=[
{"role": "user", "content": "My name is Alice"},
{"role": "assistant", "content": "Nice to meet you, Alice!"},
{"role": "user", "content": "What's my name?"}
]
)
```
The `input` array supports roles: `user`, `assistant`, `developer` (replaces `system` in newer models), and `tool`.
## The require_approval parameter and MCP options
**`require_approval: "never"`** enables fully automatic tool execution—LiteLLM returns the final response in a single API call:
```python
response = client.responses.create(
model="gpt-4o",
input="Search for Python documentation",
tools=[{
"type": "mcp",
"server_label": "search_server",
"server_url": "litellm_proxy",
"require_approval": "never" # No approval needed
}]
)
# Response includes tool results integrated into final answer
```
**Without `require_approval: "never"`**, you get an approval flow requiring two API calls:
```python
# Step 1: Get approval request
response = client.responses.create(
model="gpt-4o",
input="Search for docs",
tools=[{"type": "mcp", "server_label": "search", "server_url": "litellm_proxy"}]
)
# Extract approval request ID from response.output
approval_id = None
for output in response.output:
if output.type == "mcp_approval_request":
approval_id = output.id
break
# Step 2: Approve and get final response
final_response = client.responses.create(
model="gpt-4o",
input=[{"type": "mcp_approval_response", "approve": True, "approval_request_id": approval_id}],
previous_response_id=response.id,
tools=[{"type": "mcp", "server_label": "search", "server_url": "litellm_proxy"}]
)
```
## Restricting tools with allowed_tools
Control which MCP tools are available at **request time** or **server configuration level**:
**Request-level restriction** (per-call):
```python
tools=[{
"type": "mcp",
"server_label": "github_mcp",
"server_url": "litellm_proxy",
"require_approval": "never",
"allowed_tools": ["list_repos", "get_file_contents"] # Only these tools available
}]
```
**Server-level restriction** (in config.yaml):
```yaml
mcp_servers:
github_mcp:
url: "https://api.github.com/mcp"
allowed_tools: ["list_repos", "get_file_contents"] # Whitelist
disallowed_tools: ["delete_repo", "force_push"] # Blacklist
```
If both `allowed_tools` and `disallowed_tools` are specified, `allowed_tools` takes priority.
## Authentication headers
LiteLLM supports multiple authentication header formats:
| Header | Use Case |
|--------|----------|
| `Authorization: Bearer sk-...` | **Standard** - Used by OpenAI SDK automatically |
| `x-litellm-api-key: Bearer sk-...` | **MCP connections** and custom scenarios |
| `api-key: ...` | Azure OpenAI compatibility |
**For standard API calls** (Discord bot), use the OpenAI SDK default:
```python
client = OpenAI(
base_url="http://localhost:4000",
api_key="sk-your-key" # Sent as "Authorization: Bearer sk-your-key"
)
```
**For MCP tool headers** (when calling external MCP servers), use the `headers` parameter:
```python
tools=[{
"type": "mcp",
"server_label": "github",
"server_url": "litellm_proxy",
"require_approval": "never",
"headers": {
"x-litellm-api-key": "Bearer sk-your-litellm-key",
"x-mcp-github-authorization": "Bearer ghp_your_github_token"
}
}]
```
## Complete Discord bot migration example
Here's a full implementation pattern for migrating from chat.completions to responses with MCP:
```python
from openai import OpenAI
import os
class LiteLLMResponsesClient:
"""Client wrapper for Discord bot using LiteLLM Responses API with MCP."""
def __init__(self, proxy_url: str, api_key: str):
self.client = OpenAI(base_url=proxy_url, api_key=api_key)
self.conversations = {} # user_id -> response_id mapping
def get_mcp_tools(self, server_label: str = "default") -> list:
"""Define MCP tools configuration."""
return [{
"type": "mcp",
"server_label": server_label,
"server_url": "litellm_proxy",
"require_approval": "never",
"allowed_tools": ["search", "fetch_data", "analyze"] # Customize as needed
}]
def chat(
self,
user_id: str,
message: str,
model: str = "anthropic/claude-3-5-sonnet-latest",
use_mcp_tools: bool = True,
stream: bool = False
):
"""Send a message and get response, with optional MCP tools and streaming."""
previous_id = self.conversations.get(user_id)
kwargs = {
"model": model,
"input": message,
"stream": stream
}
if previous_id:
kwargs["previous_response_id"] = previous_id
if use_mcp_tools:
kwargs["tools"] = self.get_mcp_tools()
kwargs["tool_choice"] = "auto"
if stream:
return self._handle_stream(user_id, **kwargs)
else:
response = self.client.responses.create(**kwargs)
self.conversations[user_id] = response.id
return self._extract_text(response)
def _handle_stream(self, user_id: str, **kwargs):
"""Generator for streaming responses."""
stream = self.client.responses.create(**kwargs)
response_id = None
for event in stream:
if hasattr(event, 'type'):
if event.type == "response.created":
response_id = event.response.id
elif event.type == "response.output_text.delta":
yield event.delta
if response_id:
self.conversations[user_id] = response_id
def _extract_text(self, response) -> str:
"""Extract text from Responses API response."""
for output in response.output:
if output.type == "message":
for content in output.content:
if content.type == "output_text":
return content.text
return ""
def clear_history(self, user_id: str):
"""Clear conversation history for a user."""
self.conversations.pop(user_id, None)
# Discord bot integration example
import discord
bot = discord.Bot()
llm_client = LiteLLMResponsesClient(
proxy_url=os.environ["LITELLM_PROXY_URL"],
api_key=os.environ["LITELLM_API_KEY"]
)
@bot.event
async def on_message(message):
if message.author.bot:
return
if bot.user.mentioned_in(message):
user_id = str(message.author.id)
user_message = message.content.replace(f'<@{bot.user.id}>', '').strip()
# Non-streaming response with MCP tools
response_text = llm_client.chat(
user_id=user_id,
message=user_message,
use_mcp_tools=True
)
await message.reply(response_text)
# Run: bot.run(os.environ["DISCORD_TOKEN"])
```
## Official documentation links
- **Responses API documentation**: https://docs.litellm.ai/docs/response_api
- **MCP overview**: https://docs.litellm.ai/docs/mcp
- **MCP usage guide**: https://docs.litellm.ai/docs/mcp_usage
- **MCP permission management**: https://docs.litellm.ai/docs/mcp_control
- **OpenAI provider Responses API**: https://docs.litellm.ai/docs/providers/openai/responses_api
- **Streaming documentation**: https://docs.litellm.ai/docs/completion/stream
- **Virtual keys and auth**: https://docs.litellm.ai/docs/proxy/virtual_keys
## Key migration considerations
The Responses API is marked as **BETA** in LiteLLM. Ensure you're running LiteLLM **1.63.8+** and using OpenAI SDK **1.66.1+** for full compatibility. Model names must include the provider prefix (e.g., `openai/gpt-4o`, `anthropic/claude-3-5-sonnet-latest`). Response IDs are encrypted per-user by default for security—users cannot access other users' conversation history unless you disable this with `disable_responses_id_security: true` in config.yaml.
The primary advantage for Discord bots is the automatic MCP tool execution loop. With chat.completions, you must manually detect tool calls, execute them, and send results back. With Responses API and `require_approval: "never"`, LiteLLM handles this entire flow internally, returning the final integrated response in a single call.