LiteLLM Agent MCP Server
Give your AI agent access to 100+ LLMs.
This MCP server lets AI agents (Claude Code, Cursor, etc.) call any LLM through LiteLLM's unified API. Stop being limited to one model — use the right model for each task.
Why?
AI agents are typically stuck on a single model. With this MCP server, your agent can:
- 🔀 Call any model — GPT-4, Claude, Gemini, Mistral, and 100+ more
- ⚖️ Compare outputs — Get responses from multiple models and pick the best
- 🎯 Use the right tool — Code tasks → GPT-4, writing → Claude, long docs → Gemini
- 💰 Save costs — Route simple queries to cheaper models
Tools
| Tool | Description |
|---|---|
call |
Call any LLM model (OpenAI chat completions format) |
responses |
Use OpenAI Responses API format (stateful, tools, structured output) |
messages |
Use Anthropic Messages API format (native Claude format) |
generate_content |
Use Google generateContent format (native Gemini format) |
compare |
Compare responses from multiple models |
models |
List available models and their strengths |
recommend |
Get model recommendation for a task type |
Installation
Claude Desktop / Cursor
Add to your MCP config:
{
"mcpServers": {
"litellm": {
"command": "python",
"args": ["-m", "litellm_agent_mcp"],
"env": {
"OPENAI_API_KEY": "sk-...",
"ANTHROPIC_API_KEY": "sk-..."
}
}
}
}
From PyPI
pip install litellm-agent-mcp
From Source
git clone https://github.com/BerriAI/litellm-agent-mcp
cd litellm-agent-mcp
pip install -e .
Usage Examples
Call a specific model
Use the `call` tool:
- model: "gpt-4o"
- prompt: "Explain this code: [code here]"
Compare multiple models
Use the `compare` tool:
- models: ["gpt-4o", "claude-sonnet-4-20250514"]
- prompt: "What's the best approach to implement caching?"
Get a recommendation
Use the `recommend` tool:
- task_type: "code"
→ Returns: gpt-4o (Strong at code generation, debugging, and review)
Environment Variables
Set API keys for the providers you want to use:
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-...
GEMINI_API_KEY=...
MISTRAL_API_KEY=...
Or point to a LiteLLM proxy:
LITELLM_API_BASE=https://your-proxy.com
LITELLM_API_KEY=sk-...
Supported Models
| Provider | Models |
|---|---|
| OpenAI | gpt-4o, gpt-4o-mini, o1-preview, o1-mini |
| Anthropic | claude-sonnet-4, claude-opus-4 |
| gemini-1.5-pro, gemini-1.5-flash | |
| Mistral | mistral-large-latest |
| + 100 more | See LiteLLM docs |
License
MIT
Links
Metadata
Release files for litellm-mcp 1.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| litellm_mcp-1.0.4.tar.gz | 7.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| litellm_mcp-1.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.5 kB
Release files / litellm_mcp-1.0.4.tar.gz
| Download URL | litellm_mcp-1.0.4.tar.gz |
|---|---|
| Size | 7.5 kB |
| Tags | Source |
|
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Release files / litellm_mcp-1.0.4-py3-none-any.whl
| Download URL | litellm_mcp-1.0.4-py3-none-any.whl |
|---|---|
| Size | 8.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/6.2.0 CPython/3.11.2
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