Demo Your First LangChain MCP Agent

Welcome back. This lesson runs a small Python example that uses LangChain to drive an agent which interacts with an MCP server. The example shows how a simple prompt can cause the agent to pick MCP tools, perform a kubectl-like operation against a cluster, and return a human-friendly plain-text answer produced by the LLM.

What you’ll learn

How to start an MCP server container configured to talk to a local KinD cluster
How the sample agent discovers and invokes MCP tools
How the LLM converts raw tool output into a readable report

Setup

Export the kubeconfig for the kind cluster and save it for use inside Docker:

kind get kubeconfig --name finops-cluster --internal > /tmp/kubeconfig-docker.yaml

Start the MCP container so it can access the kind network. Replace IMAGE with your MCP server image name:

docker run --rm -p 8000:8000 --network kind \
  -v /tmp/kubeconfig-docker.yaml:/home/appuser/.kube/config:ro \
  -e ENABLE_UNSAFE_STREAMABLE_HTTP_TRANSPORT=1 \
  -e PORT=8000 \
  -e HOST=0.0.0.0 \
  IMAGE

Files in this example

File	Purpose
`query_agent.py`	Small entrypoint script that sends a prompt to the LangChain agent and prints the result.
`mcp_client.py`	MCP-specific wrapper that handles server connection, tool discovery, tool invocation, and integration with the LLM.

The script we run

Here’s the visible entrypoint script (query_agent.py). It builds a short prompt and calls into the MCP client to run the agent asynchronously.

"""Prompt -> LangChain agent -> MCP tools -> plain-text answer."""
import asyncio

from mcp_client import run_agent

PROMPT = "List all namespaces"

async def main() -> None:
    print(await run_agent(PROMPT))

if __name__ == "__main__":
    asyncio.run(main())

This prompt is intentionally minimal to demonstrate the end-to-end flow. For production use, prefer structured prompt templates and explicit instructions so outputs are consistent and auditable.

High-level flow (what the script does)

Builds the prompt (here: “List all namespaces”).
Calls run_agent, which:
- Connects to the configured MCP server.
- Enumerates available tools exposed by the MCP server.
- Selects an appropriate tool (for example, kubectl_get).
- Executes the tool against the target cluster and captures the result (JSON or text).
- Sends the raw tool result to the LLM to create a human-friendly report.
Prints the final output to stdout.

Run the agent

From the project directory run:

python3 query_agent.py

Observed logs and output

Below is an example of the logs and the final LLM-generated answer observed when running the script against a local MCP listening on http://localhost:8000/mcp. These logs show connection setup, tool discovery, tool invocation, and the final LLM call.

[INFO] MCP server "k8s": initializing with: {'url': 'http://localhost:8000/mcp', 'transport': 'streamable_http'}
[INFO] MCP server "k8s": Pre-validating authentication
[INFO] HTTP Request: POST http://localhost:8000/mcp "HTTP/1.1 200 OK"
[INFO] MCP server "k8s": Authentication validation passed: 200
[INFO] MCP server "k8s": connecting via Streamable HTTP (explicit) to http://localhost:8000/mcp
[INFO] HTTP Request: POST http://localhost:8000/mcp "HTTP/1.1 200 OK"
[INFO] Negotiated protocol version: 2025-11-25
[INFO] MCP server "k8s": connected
[INFO] HTTP Request: POST http://localhost:8000/mcp "HTTP/1.1 202 Accepted"
[INFO] HTTP Request: POST http://localhost:8000/mcp "HTTP/1.1 200 OK"
[INFO] MCP server "k8s": 8 tool(s) available:
[INFO] - kubectl_get
[INFO] - kubectl_describe
[INFO] - kubectl_logs
[INFO] - kubectl_context
[INFO] - kubectl_reconnect
[INFO] - explain_resource
[INFO] - list_api_resources
[INFO] - ping
[INFO] MCP servers initialized: 8 tool(s) available in total
[INFO] HTTP Request: POST https://api.ai.kodekloud.com/v1/chat/completions "HTTP/1.1 200 OK"
[INFO] MCP tool "k8s"/"kubectl_get" received input: {'resourceType': 'namespaces', 'name': None, 'namespace': 'default', 'output': 'json', 'allNamespaces': False, 'labelSelector': None, 'fieldSelector': None, 'sortBy': None, 'context': ''}
[INFO] HTTP Request: POST http://localhost:8000/mcp "HTTP/1.1 200 OK"
[INFO] MCP tool "k8s"/"kubectl_get" received result (size: 1618)
[INFO] HTTP Request: POST https://api.ai.kodekloud.com/v1/chat/completions "HTTP/1.1 200 OK"
[INFO] MCP server "k8s": session closed

Here are all the namespaces in the cluster:

1. **airline** - Active (Created: 2026-07-11T04:37:20Z)
2. **booking-api** - Active (Created: 2026-07-11T04:37:20Z)
3. **default** - Active (Created: 2026-07-11T04:36:53Z)
4. **flight-search** - Active (Created: 2026-07-11T04:37:20Z)
5. **inventory** - Active (Created: 2026-07-11T04:37:20Z)
6. **kube-node-lease** - Active (Created: 2026-07-11T04:36:52Z)
7. **kube-public** - Active (Created: 2026-07-11T04:36:52Z)
8. **kube-system** - Active (Created: 2026-07-11T04:36:52Z)
9. **local-path-storage** - Active (Created: 2026-07-11T04:36:57Z)
10. **payment** - Active (Created: 2026-07-11T04:37:20Z)

The cluster has 10 namespaces total. The first 4 system namespaces (kube-node-lease, kube-public, kube-system, local-path-storage) are Kubernetes infrastructure namespaces, while the remaining 6 (airline, booking-api, default, flight-search, inventory, payment) appear to be application-specific namespaces.
(.venv) ➜ k8-finops-agent git:(main) *

Quick notes on the flow

The agent does not run a local kubectl in your environment. Instead, it invokes MCP tools (for example, kubectl_get) that run on the MCP server and operate against the cluster configured for that server.
Raw tool output is forwarded to the LLM, which generates the final human-friendly report printed by the script.
The LLM endpoint shown in the logs (e.g., https://api.ai.kodekloud.com/...) is environment-specific. Configure the LLM provider you use (OpenAI, Anthropic, a self-hosted model, etc.) in your environment.

Example MCP tools discovered

Tool name	Typical use
`kubectl_get`	Retrieve Kubernetes resources (pods, namespaces, services) in JSON/text
`kubectl_describe`	Show detailed resource descriptions
`kubectl_logs`	Fetch container logs
`kubectl_context`	Inspect or switch cluster context
`kubectl_reconnect`	Re-establish a connection to the cluster
`explain_resource`	Provide explanations for a resource kind
`list_api_resources`	Enumerate available API resources on the cluster
`ping`	Health check / connectivity test

Next steps and practical extensions

For FinOps or governance agents, collect labels and annotations across workloads and services. Use structured prompts and scripts to aggregate tags and apply FinOps rules or policy checks.
Expand the agent’s prompt template to ask for specific data (e.g., costs, owner tags, environment labels) and produce CSV or JSON summaries suitable for automation.
Add unit tests or integration tests for mcp_client.py to validate tool selection and result parsing.

Links and references

If you want, I can also:

Show an expanded mcp_client.py example demonstrating tool discovery and invocation.
Provide a more robust prompt template for production FinOps agents.

Introduction

The Problem Statement

Introduction to FinOps

Setting Up SkyLine Air Kubernetes Services

MCP Gateway for Kubernetes

LangChain MCP Agents

LLM Policy-Aware Label Audit

From Findings to Actionable Tickets

Demo Your First LangChain MCP Agent

What you’ll learn

Setup

Files in this example

The script we run

High-level flow (what the script does)

Run the agent

Observed logs and output

Quick notes on the flow

Example MCP tools discovered

Next steps and practical extensions

Links and references

Watch Video

Practice Lab

​What you’ll learn

​Setup

​Files in this example

​The script we run

​High-level flow (what the script does)

​Run the agent

​Observed logs and output

​Quick notes on the flow

​Example MCP tools discovered

​Next steps and practical extensions

​Links and references

Watch Video

Practice Lab

What you’ll learn

Setup

Files in this example

The script we run

High-level flow (what the script does)

Run the agent

Observed logs and output

Quick notes on the flow

Example MCP tools discovered

Next steps and practical extensions

Links and references