Can I get the definition of an obscure word like 'synecdoche'?

Yes! Use the `get_word_definition` tool with the word 'synecdoche'. It will return the definitions, examples, and the part of speech from Wiktionary.

How do I see the part of speech for a word?

The `get_word_definition` tool automatically identifies if a word is a noun, verb, adjective, etc., and provides definitions for each relevant part of speech.

Does it provide a summary for general topics?

Yes. Use the `get_word_summary` tool to retrieve a concise description of a word that also functions as an encyclopedic topic, suitable for quick overviews.

Wiktionary MCP Server for Pydantic AI 2 tools — connect in under 2 minutes

Name: Wiktionary
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 2 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Wiktionary through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Wiktionary "
            "(2 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Wiktionary?"
    )
    print(result.data)

asyncio.run(main())

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About Wiktionary MCP Server

Equip your AI agent with the power of the world's most comprehensive collaborative dictionary through the Wiktionary MCP server. This integration provides instant access to linguistic data for thousands of words and phrases. Your agent can retrieve precise definitions, identify parts of speech (nouns, verbs, adjectives), see usage examples, and get concise summaries for encyclopedic topics. Whether you are improving your writing, translating complex texts, or exploring etymology, your agent acts as a dedicated philologist and lexicographer through natural conversation.

Pydantic AI validates every Wiktionary tool response against typed schemas, catching data inconsistencies at build time. Connect 2 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Word Definitions — Retrieve detailed linguistic definitions and parts of speech.
Encyclopedic Summaries — Get concise descriptions for words that also function as general topics.
Linguistic Examples — View real-world usage examples for better understanding of context.
Multilingual Support — Access definitions and data across various languages supported by the platform.
Etymology Auditing — Explore the history and origin of words across different linguistic roots.

The Wiktionary MCP Server exposes 2 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Wiktionary to Pydantic AI via MCP

Follow these steps to integrate the Wiktionary MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 2 tools from Wiktionary with type-safe schemas

Why Use Pydantic AI with the Wiktionary MCP Server

Pydantic AI provides unique advantages when paired with Wiktionary through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Wiktionary integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Wiktionary connection logic from agent behavior for testable, maintainable code

Wiktionary + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Wiktionary MCP Server delivers measurable value.

Type-safe data pipelines: query Wiktionary with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Wiktionary tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Wiktionary and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Wiktionary responses and write comprehensive agent tests

Wiktionary MCP Tools for Pydantic AI (2)

These 2 tools become available when you connect Wiktionary to Pydantic AI via MCP:

get_word_definition

Get the definition of a word

get_word_summary

Get a short summary of a word or topic

Example Prompts for Wiktionary in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Wiktionary immediately.

"What is the definition of the word 'ephemeral'?"

"Give me a summary of 'Computer Science'."

"Identify the part of speech for 'serendipity'."

Troubleshooting Wiktionary MCP Server with Pydantic AI

Common issues when connecting Wiktionary to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Wiktionary + Pydantic AI FAQ

Common questions about integrating Wiktionary MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Wiktionary MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.