Un servidor Model Context Protocol mínimo, construido con el SDK oficial de Python. Expone una única herramienta, define, que busca la definición de una palabra en inglés usando la API gratuita de dictionaryapi.dev.
Este proyecto se creó como ejercicio de aprendizaje para entender cómo se estructuran los servidores MCP, cómo se comunican con los clientes y cómo se implementan públicamente.
Expone una única herramienta: define(word: str) -> str
Dada una palabra, devuelve su categoría gramatical y su definición
No requiere clave de API ni autenticación
Related MCP server: yifanli-mcp-add-demo
Python 3.10+
uv (recomendado) o pip
# 1. Clone this repo
git clone <your-repo-url>
cd my-mcp-server
# 2. Install dependencies
uv syncOpción A — Inspector MCP (herramienta de prueba visual, la más sencilla para principiantes):
uv run mcp dev server.pyEsto abre un inspector basado en navegador donde puedes llamar a la herramienta define directamente y ver la solicitud/respuesta sin necesidad de un cliente de IA completo.
Opción B — Conectar con Claude Desktop:
Añade esto a tu archivo de configuración de Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"dictionary-lookup": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/my-mcp-server",
"run",
"server.py"
]
}
}
}Reinicia Claude Desktop y luego pregunta algo como: "Usa la herramienta de diccionario para definir 'ephemeral'."
Entrada: define("ephemeral")
Salida: ephemeral (adjective): lasting for a very short time.
Este servidor está publicado en Smithery: <link una vez desplegado>
Para desplegar tu propia copia:
Sube este repositorio a GitHub.
Asegúrate de que server.py sea compatible con el transporte streamable-http para el alojamiento remoto.
Alójalo (p. ej., Render/Railway) o usa el flujo de publicación de Smithery.
En Smithery, elige "Publicar vía URL" y apunta a la URL de tu servidor alojado.
Creado para aprender los fundamentos de MCP: cómo un cliente descubre las herramientas de un servidor, cómo se realiza y se responde una llamada a una herramienta, y cómo un servidor en funcionamiento se comparte públicamente a través de un marketplace como Smithery.
Look up the definition of an English word and return it as text.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full behavioral burden. It conveys a read-only lookup behavior by saying 'Look up' and specifies the return type as 'text.' It does not cover edge cases like unknown words or multiple definitions, but these are not critical for a simple dictionary lookup tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the action, scope, and output. Every word earns its place and there is no unnecessary elaboration or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only lookup tool with an output schema, the description is substantially complete. It covers the input type ('English word'), the operation, and the return format ('text'). It does not mention failure behavior, but that is a minor gap given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only has a parameter titled 'Word' with zero description coverage. The description compensates by specifying that the word must be an English word, which adds meaningful semantic context beyond the schema. Though format and edge cases are not detailed, the parameter and its expected value are straightforward.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Look up') and a clear resource ('the definition of an English word'), and it states the output form ('return it as text'). There are no sibling tools, so no differentiation is needed. This makes the tool's purpose immediately obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool: whenever an agent needs to retrieve the meaning of an English word. It also implicitly excludes non-English words. Because there are no sibling tools or alternatives, explicit when-not-to-use guidance is not necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0defineScored across 1 tool
There is only one tool, so there is no possibility of confusing it with another tool. Its purpose is immediately clear and unambiguous.
The single tool name 'define' is a clear imperative verb that matches its action. With only one tool, there are no mixed naming conventions or inconsistencies.
One tool feels thin for a server labeled as a dictionary lookup service, though it does cover the core definition lookup need. It is borderline rather than extreme.
The core lookup functionality is covered well, but a dictionary server might reasonably include related operations like synonyms, usage examples, or pronunciation. These are minor gaps rather than fatal omissions.
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
A Model Context Protocol server for Wix AI tools
The official Model Context Protocol server for Ambee. It gives any MCP-compatible AI assistant — Claude, ChatGPT, Cursor, VS Code, Ollama, and more direct access to live air quality, pollen, and weather data. To get started, including information on signing up and obtaining your Ambee key, check out the Ambee documentation on https://docs.ambeedata.com
