How to Use the Howspace MCP in Pydantic AI

Build type-safe Howspace integrations using Pydantic AI to validate workspace and participant data at runtime.

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Connect Howspace MCP to Pydantic AI

Create your Vinkius account to connect Howspace 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 Howspace with Pydantic AI

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Type-safe workspace creation using Pydantic AI

This MCP Server exposes `create_workspace` to your Pydantic AI agent with strict runtime validation. The tool ensures that Howspace configurations match your exact Pydantic models before making the API call. Using this with Pydantic AI prevents malformed Howspace workspace names or invalid settings from hitting your account. Your agent checks existing environments using `list_workspaces` with guaranteed type safety.

Validate participant data during ingestion

Your Pydantic AI agent calls `add_participant` only after validating the email and user details against your Pydantic schemas. It uses `list_participants` to pull Howspace rosters that are guaranteed to match your expected Python types. This eliminates silent errors in your Pydantic AI pipelines. If the Howspace server returns unexpected participant fields, your application catches the validation error immediately.

Structure campaign data for reliable pipelines

Run `list_campaigns` to fetch Howspace organizational development initiatives as strongly-typed objects. The agent queries `get_workspace` to build a clean, validated map of your entire Howspace infrastructure. This structured approach keeps your Pydantic AI agents operating on clean Howspace data. You can confidently pass these validated campaign lists to downstream microservices.

Setup guide

Set up Howspace 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": {
        "howspace-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Howspace MCP in Pydantic AI

Use the MCPToolset class pointing to the server URL and pass it to your Agent. This registers tools like `list_workspaces` and `get_me` as validated tool calls.

Your Pydantic AI agent raises a validation error immediately instead of passing bad data. This ensures your local rosters from `list_participants` remain strictly typed.

Yes, you can filter the toolset during initialization to only expose specific tools like `add_participant`. This keeps your Pydantic AI agent focused on a single task.

Yes, the server runs in a secure sandbox, and your Pydantic AI client connects to it via HTTP or SSE transport.

Yes, all workspace names and participant email addresses are processed in transit. The MCP Server operates inside a zero-trust sandbox and never writes your data to disk.

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