How to Use the Crowdin MCP in Pydantic AI

Q: Can I use Pydantic AI with local Crowdin files?

You can process file structure data retrieved by listprojectfiles and validate it against your local file system state.

Build type-safe localization agents with Pydantic AI and the Crowdin MCP server.

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Connect Crowdin MCP to Pydantic AI

Create your Vinkius account to connect Crowdin to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Crowdin with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strict type validation for Pydantic AI

Every tool call like `get_file_details` returns data that maps cleanly to your defined models. You define the structure, and the agent enforces it at runtime. This prevents silent errors during translation status updates. If the API provides unexpected output, your agent catches the mismatch immediately.

Automated workflow oversight

Use `list_project_tasks` to track linguist assignments and proofreading status. Your agent validates these task objects against your Pydantic schemas. This ensures your localization pipeline stays predictable. You eliminate the risk of processing malformed data from the API.

Precise localization asset control

Manage your translation memories and glossaries via `list_translation_memories`. Your agent checks these against local constraints before applying them to new strings. This approach guarantees that only valid translation data enters your system. Your Pydantic AI agent maintains high integrity across your entire localization stack.

Setup guide

Set up Crowdin MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "crowdin-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Crowdin tools.",
)

result = await agent.run("List recent Crowdin transactions")
print(result.output)

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Common questions about Crowdin MCP in Pydantic AI

Define your models using Pydantic, and the agent will validate the responses from this MCP server automatically at runtime.

Yes. Install the slim version with MCP support and pass the server endpoint to your agent's toolsets.

The agent will fail with a validation error. This prevents corrupted data from polluting your localization workflow.

The server operates externally. Your authentication details are handled by the Vinkius infrastructure, and only the necessary metadata is passed to your agent.

You can process file structure data retrieved by `list_project_files` and validate it against your local file system state.

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