e2e-mcp-test
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@e2e-mcp-testList all the items currently in the system"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
e2e-mcp-test
E2E test MCP server
Overview
This is a FastMCP server that exposes a REST API as a set of MCP (Model Context Protocol) tools. AI agents and LLMs can invoke these tools to interact with the upstream API.
Local Development
Prerequisites
Python 3.11+
The upstream API running at
http://localhost:8080
Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txtRun
python3 mcp_server.pyThe server starts on http://0.0.0.0:8000/mcp using the
streamable HTTP transport.
Container Build
docker build -t e2e-mcp-test .
docker run -p 8000:8000 e2e-mcp-testCI/CD with Pipelines as Code (Tekton)
This project uses Pipelines as Code (PAC) for fully automated CI/CD on
OpenShift. No manual setup is required -- every push to main automatically
builds the container image and deploys to OpenShift.
The pipeline definition lives in .tekton/push.yaml and performs:
Clone -- fetches the repository source
Build -- builds the container image with buildah and pushes to the OpenShift internal registry
Deploy -- applies the deployment manifests and rolls out the new version
How It Works
A GitHub webhook (created automatically by the RHDH template) sends push events
to the Pipelines as Code controller on OpenShift. PAC reads .tekton/push.yaml
from the repo and creates a PipelineRun automatically.
Manual Deployment (without pipeline)
Apply the included manifests directly:
oc apply -f deploy/deployment.yamlThis creates a Deployment, Service, and Route in the mcp-servers
namespace. The Route provides a TLS-terminated public endpoint.
Customization
Edit mcp_server.py to replace the placeholder tools (list_items,
get_item, create_item) with tools that match your actual API endpoints.
Each @mcp.tool function maps to one REST endpoint:
HTTP Method | MCP Tool Pattern |
GET (list) | Tool that returns a list of resources |
GET (by id) | Tool that returns a single resource |
POST | Tool that creates a resource |
PATCH / PUT | Tool that updates a resource |
DELETE | Tool that removes a resource |
This server cannot be deployed
Related MCP Connectors
MCP server for building and testing AI agents with multi-model experimentation and insights.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
MCP server for secureFlows: token-free URL builders and integration-linting tools for AI agents.