mcp-agent

mcp-agent is a Python-based MCP server that provides a ready-to-run agent runtime built around the Model Context Protocol. It lets you deploy and manage agents as MCP servers, enabling patterns such as map-reduce, orchestrator, evaluator-optimizer, and routing through a composable, durable execution stack. The project ships with a minimal working example and integrates with the MCP ecosystem to manage server lifecycles, secrets, and configuration so you can focus on building robust agent logic. You can run the MCP server via the UVX CLI (uvx mcp-agent) and connect agents, LLMs, and workflows to it to execute complex tasks end-to-end.

To start, install and scaffold a project using the UVX tooling, initialize an MCP server project, and then deploy or run the server. The server exposes capabilities to attach agents, wire LLMs, and compose workflows, all under the MCP abstraction so your components can talk a common protocol.

When running, you can use the included examples and documentation to learn how to define agents, configure LLMs, and orchestrate tools and prompts in a principled MCP-based flow.

Prerequisites:

• Python 3.8+ (or a compatible Python runtime for your environment)
• uvx CLI tooling installed (part of the UVX workflow) to manage MCP servers
• Internet access to install packages from PyPI and the UVX registry

Installation steps:

1. Install Python and verify

python3 --version
pip3 --version

2. Install UVX CLI (if not already installed) and ensure it is in PATH

# Example using npm/uvx installer pattern (adjust per your environment)
# If uvx is provided via a separate installer, follow those steps
# Here we assume a generic installer script or package manager
# e.g., npm install -g uvx  (if the distribution uses npm) or follow official UVX install guide

3. Create and scaffold the MCP server project for mcp-agent

uvx mcp-agent init
cd your-mcp-agent-project

4. Install the Python package for the MCP server (within your environment)

pip3 install mcp-agent

5. Run or deploy the MCP server locally (using UVX)

uvx mcp-agent run

6. Optional: initialize configuration and secrets for your deployment as described in the docs

uvx mcp-agent config init

Note: Replace placeholder commands with the exact UVX/installer commands your environment provides. Refer to the official MCP Agent docs for the precise install commands and versions compatible with your setup.

Tips and common considerations:

• The mcp-agent server is Python-based and leverages MCP for agent orchestration; ensure your Python environment is isolated (virtualenv/venv) to avoid dependency conflicts.
• Secrets (like LLM API keys) should be provided via environment variables or a secrets file as documented by the project (e.g., mcp_agent.secrets.yaml).
• When deploying to cloud or durable runtimes, you may want to configure Temporal or other backends as described in the docs to enable pause/resume and fault tolerance.
• The CLI (uvx mcp-agent) supports init, deploy, and run flows; consult the docs for environment-variable flags and server management commands.
• If you intend to publish or share your MCP server, ensure your environment variables and secret handling code paths are secure and that you follow best practices for secrets management.