MCP Search Server

# MCP Agent Intent Classification Workflow example This example shows using intent classification workflow, which is a close sibling of the [router workflow](../workflow_router/). The example uses both the OpenAI embedding intent classifier and the OpenAI LLM intent classifier. ## `1` App set up First, clone the repo and navigate to the workflow intent classifier example: ```bash git clone https://github.com/lastmile-ai/mcp-agent.git cd mcp-agent/examples/workflows/workflow_intent_classifier ``` Install `uv` (if you don’t have it): ```bash pip install uv ``` Sync `mcp-agent` project dependencies: ```bash uv sync ``` Install requirements specific to this example: ```bash uv pip install -r requirements.txt ``` ## `2` Set up environment variables Copy and configure your secrets and env variables: ```bash cp mcp_agent.secrets.yaml.example mcp_agent.secrets.yaml ``` Then open `mcp_agent.secrets.yaml` and add your OpenAI api key. ## (Optional) Configure tracing In `mcp_agent.config.yaml`, you can set `otel` to `enabled` to enable OpenTelemetry tracing for the workflow. You can [run Jaeger locally](https://www.jaegertracing.io/docs/2.5/getting-started/) to view the traces in the Jaeger UI. ## `3` Run locally Run your MCP Agent app: ```bash uv run main.py ```