How to Use the Mode (Collaborative Data Platform) MCP in Pydantic AI

Build type-safe Pydantic AI workflows that validate your Mode workspace reports and space metadata at runtime.

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Connect Mode (Collaborative Data Platform) MCP to Pydantic AI

Create your Vinkius account to connect Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) with Pydantic AI

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Validate Mode reports with Pydantic AI

The `list_reports` tool returns a structured list of all static data reports generated within your Mode workspace. Pydantic AI parses this payload against strict Python schemas, ensuring every report token and title matches your defined models. If you need to pinpoint a specific asset, the agent uses `search_reports` to query the Mode API. The Pydantic AI framework validates the search results at runtime, catching any unexpected null fields before they break your pipeline. The MCP server ensures data structure consistency.

Inspect spaces and analytical environments safely

The `list_spaces` tool retrieves all accessible collection spaces that isolate datasets across your Mode workspace. The Pydantic AI agent uses this tool to map out your team's organizational structure with guaranteed type safety. When targeting a specific space, `get_space` pulls detailed metadata that is immediately validated by Pydantic AI. This prevents your agent from processing corrupted space parameters or missing IDs, failing loudly if the API payload deviates.

Audit active database connectors and workspace members

The `list_data_sources` tool exposes the active database and warehouse connections bound to your Mode analytical platform. This allows your Pydantic AI agent to programmatically verify that reports are only pulling from authorized, type-validated data sources. To verify workspace access, the agent calls `list_members` to get a structured list of joined analytical users in Mode. The Pydantic AI framework guarantees that user emails, IDs, and roles match your internal system models perfectly.

Setup guide

Set up Mode (Collaborative Data Platform) 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": {
        "mode-collaborative-data-platform-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Mode (Collaborative Data Platform) transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mode. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Mode (Collaborative Data Platform) MCP in Pydantic AI

Use the unified toolset class with the Vinkius HTTP endpoint. Pass the toolset directly into your Python agent constructor to register all 7 tools with runtime validation.

The framework will raise a validation error instantly. This prevents your agent from operating on unexpected or malformed metadata.

Yes, because the framework is model-agnostic, you can use these tools with local models or commercial APIs. The validation layer remains identical.

It supports both Streamable HTTP and SSE transports, allowing you to connect to the managed Vinkius server over secure, persistent connections.

All data processed by the data source tool is handled within a zero-trust, ephemeral V8 sandbox. No database credentials or report tokens are cached or stored.

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