How to Use the Coder (Remote Dev) MCP in Pydantic AI

Q: How do I set up Coder (Remote Dev) with Pydantic AI?

Install the pydantic-ai-slim package and use the MCPToolset class. Point it at your Vinkius-hosted endpoint to begin making type-safe calls.

Q: How do I list workspaces using Pydantic AI?

Call the listworkspaces tool through the MCPToolset. The framework will automatically validate the returned list against your defined workspace model.

Build type-safe Coder (Remote Dev) automation with Pydantic AI and validated MCP responses.

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Connect Coder (Remote Dev) MCP to Pydantic AI

Create your Vinkius account to connect Coder (Remote Dev) 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 Coder (Remote Dev) with Pydantic AI

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Perplexity

Runtime validated workspace actions

Pydantic AI ensures every response from the server matches your expected schema. If `get_workspace` returns malformed data, your agent stops immediately. This prevents silent failures when managing complex environments. You get strict type safety for every `create_workspace_port_share` or `update_workspace_acl` call.

Secure identity and token management

Manage user secrets and tokens with full validation. Your agent calls `create_user_secret` and `create_user_token` while adhering to your defined data models. This approach guarantees that no garbage data enters your identity management system. The agent handles `list_user_tokens` to audit active access keys.

Precise container status updates

Get accurate, structured data about your containers using `get_agent_containers`. Pydantic AI parses the response into clean Python objects for your agent to process. It makes managing `update_agent_app_status` reliable. You know exactly what state your services are in at any given time.

Setup guide

Set up Coder (Remote Dev) 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": {
        "coder-remote-dev-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Coder (Remote Dev) transactions")
print(result.output)

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Common questions about Coder (Remote Dev) MCP in Pydantic AI

Install the pydantic-ai-slim package and use the `MCPToolset` class. Point it at your Vinkius-hosted endpoint to begin making type-safe calls.

It provides runtime validation for every response. You avoid hallucinated fields because the agent fails if the server output doesn't match your Pydantic models.

Yes, the logs are fetched as strings that your agent processes. Because you define the response model, you control how the agent interprets the log data.

Call the `list_workspaces` tool through the `MCPToolset`. The framework will automatically validate the returned list against your defined workspace model.

Your data is validated in memory within your agent process. The MCP Server ensures that only authorized requests reach the API, keeping your workspace configurations isolated.

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