Mapbox MCP Server

# Code based on smolagents example from: https://github.com/huggingface/smolagents/blob/main/examples/agent_from_any_llm.py import os from mcp import StdioServerParameters from smolagents import ( CodeAgent, InferenceClientModel, LiteLLMModel, OpenAIServerModel, TransformersModel, tool, ToolCollection ) # Choose which inference type to use! available_inferences = ["inference_client", "transformers", "ollama", "litellm", "openai"] # chosen_inference = "inference_client" chosen_inference = "openai" print(f"Chose model: '{chosen_inference}'") if chosen_inference == "inference_client": model = InferenceClientModel(model_id="meta-llama/Llama-3.3-70B-Instruct", provider="nebius") elif chosen_inference == "transformers": model = TransformersModel(model_id="HuggingFaceTB/SmolLM2-1.7B-Instruct", device_map="auto", max_new_tokens=1000) elif chosen_inference == "ollama": model = LiteLLMModel( model_id="ollama_chat/llama3.2", api_base="http://localhost:11434", # replace with remote open-ai compatible server if necessary api_key="your-api-key", # replace with API key if necessary num_ctx=8192, # ollama default is 2048 which will often fail horribly. 8192 works for easy tasks, more is better. Check https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator to calculate how much VRAM this will need for the selected model. ) elif chosen_inference == "litellm": # For anthropic: change model_id below to 'anthropic/claude-3-7-sonnet-latest' model = LiteLLMModel(model_id="gpt-4o") elif chosen_inference == "openai": # For anthropic: change model_id below to 'anthropic/claude-3-7-sonnet-latest' model = OpenAIServerModel(model_id="gpt-4.1-2025-04-14") # Make sure there is an access token available if os.environ.get("MAPBOX_ACCESS_TOKEN", None) is None: raise EnvironmentError("To use Mapbox MCP you need to export `MAPBOX_ACCESS_TOKEN` environmental variable.") # Run server with node # alternatively you can use command="docker" and args=["run", "-i", "--rm", "mapbox-mcp-server"] server_parameters = StdioServerParameters( command="/Users/username/.nvm/versions/node/v22.3.0/bin/node", args=["/YOUR_PATH_TO_REPOSITORY/dist/index.js"], env={"MAPBOX_ACCESS_TOKEN": os.environ["MAPBOX_ACCESS_TOKEN"]}, ) # Connect to MCP, create agent with MCP's tool and run it with ToolCollection.from_mcp(server_parameters, trust_remote_code=True) as tool_collection: agent = CodeAgent(tools=tool_collection.tools, model=model, verbosity_level=2, stream_outputs=True) print("CodeAgent:", agent.run("How long does it take to drive from Big Ben to Eiffel Tower?"))