F
license-
qualityC
maintenanceA stateless Model Context Protocol server that implements a simple echo functionality with resource, tool, and prompt components, enabling LLMs to echo back messages through standardized MCP interactions.
1
mcp-server
by mfalcirolli1
Click on "Install 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., "@mcp-serverlist all available tools"
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.
mcp-server
https://gofastmcp.com/deployment/running-server
Related MCP server: my-mcp-server2
https://pypi.org/project/fastmcp/
python -m venv .venv source .venv/Scripts/activate
pip install fastmcp==2.14.1
Para PROMPTS:
Os prompts definem modelos de mensagens reutilizáveis para orientar as interações do LLM
"method": "prompts/list" // Lista todos os prompts "method": "prompts/get" // Obtém um prompt específico
Para TOOLS:
As ferramentas permitem que os LLMs executem ações executando suas funções Python. Ideal para cálculos, chamadas de API. As ferramentas podem retornar vários tipos, incluindo texto, objetos serializáveis em JSON e até mesmo imagens ou áudio auxiliados pelas classes auxiliares de mídia FastMCP.
"method": "tools/list" // Lista todas as ferramentas "method": "tools/call" // Chama uma ferramenta
Para RESOURCES:
Os recursos expõem fontes de dados somente leitura
"method": "resources/list" // Lista recursos "method": "resources/read" // Lê um recurso específico
This server cannot be installed
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
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- Alicense-qualityDmaintenanceEnables MCP clients to connect to LLM/API services using the Model Context Protocol, providing real-time interaction and tool access. Also offers RESTful API endpoints via FastAPI for programmatic integration with existing systems.Apache 2.0
- Flicense-qualityDmaintenanceEducational example of an MCP server built with FastMCP, demonstrating how to expose tools, resources, and prompts for AI clients.
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