Ein minimaler Model Context Protocol-Server, erstellt mit dem offiziellen Python SDK. Er stellt ein Tool namens define bereit, das die Definition eines englischen Wortes über die kostenlose dictionaryapi.dev-API nachschlägt.
Dieses Projekt wurde als Lernübung erstellt, um zu verstehen, wie MCP-Server aufgebaut sind, wie sie mit Clients kommunizieren und wie man einen öffentlich bereitstellt.
Stellt ein einzelnes Tool bereit: define(word: str) -> str
Gibt für ein Wort die Wortart und die Definition zurück
Kein API-Schlüssel oder Authentifizierung erforderlich
Related MCP server: yifanli-mcp-add-demo
Python 3.10+
uv (empfohlen) oder pip
# 1. Clone this repo
git clone <your-repo-url>
cd my-mcp-server
# 2. Install dependencies
uv syncOption A – MCP Inspector (visuelles Testwerkzeug, am einfachsten für Anfänger):
uv run mcp dev server.pyDies öffnet einen browserbasierten Inspector, in dem Sie das define-Tool direkt aufrufen und die Anfrage/Antwort sehen können, ohne einen vollständigen AI-Client zu benötigen.
Option B – Mit Claude Desktop verbinden:
Fügen Sie dies zu Ihrer Claude-Desktop-Konfigurationsdatei (claude_desktop_config.json) hinzu:
{
"mcpServers": {
"dictionary-lookup": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/my-mcp-server",
"run",
"server.py"
]
}
}
}Starten Sie Claude Desktop neu und fragen Sie dann etwa: „Verwenden Sie das Wörterbuch-Tool, um ‚ephemeral‘ zu definieren."
Eingabe: define("ephemeral")
Ausgabe: ephemeral (adjective): lasting for a very short time.
Dieser Server ist auf Smithery veröffentlicht: <link once deployed>
Um eine eigene Kopie bereitzustellen:
Übertragen Sie dieses Repository auf GitHub.
Stellen Sie sicher, dass server.py den streamable-http-Transport für Remote-Hosting unterstützt.
Hosten Sie es (z. B. Render/Railway) oder nutzen Sie den Veröffentlichungsprozess von Smithery.
Wählen Sie auf Smithery „Publish via URL“ und zeigen Sie auf den Endpunkt Ihres gehosteten Servers.
Erstellt, um die Grundlagen von MCP zu lernen: wie ein Client die Tools eines Servers entdeckt, wie ein Tool-Aufruf durchgeführt und beantwortet wird und wie ein funktionierender Server über einen Marktplatz wie Smithery öffentlich geteilt wird.
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.
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