FastMCP Example
A minimal Python MCP server with tool endpoints and Playwright Page Object Model tests.
README
FastMCP Example
A minimal Python FastMCP-style server with a single tool endpoint and Playwright Page Object Model (POM) tests.
Project structure
fastmcp/- package implementing the FastMCP server and tool registryfastmcp/tools/- individual tool implementationstests/- unit tests, browser tests, and BDD scenariostests/pages/- Playwright page object classestests/features/- BDD feature filestests/steps/- BDD step definitionstests/utils/- shared browser and server helpersfastmcp/tools/external_api_tool.py- example business intelligence tool using an external API
Setup
- Install Python 3.10+ and ensure it is on your PATH.
- Install the Allure CLI separately so you can view test reports.
- From the project root:
python -m pip install --upgrade pip
python -m pip install -e .[dev]
python -m playwright install chromium
Run the server
python server.py
Open http://127.0.0.1:8000 to verify the serve running.
FastMCP endpoints
GET /- server statusGET /tools- registered tool metadataPOST /tool/{tool_name}- invoke a tool
The new external_api tool can be called at /tool/external_api and returns a simple business intelligence summary from a public API.
Example request:
curl -X POST http://127.0.0.1:8000/tool/echo \
-H "Content-Type: application/json" \
-d '{"prompt":"hello"}'
Tests
Run the existing pytest suite:
pytest
Generate Allure results:
pytest --alluredir=allure-results
View the report locally after installing the Allure CLI:
allure serve allure-results
Or generate a static HTML report:
allure generate allure-results --clean -o allure-report
allure open allure-report
Page Object Model
The browser tests use a dedicated POM class at tests/pages/root_page.py.
That keeps UI interactions separate from test assertions and makes the suite easier to extend.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.