How to Use the GetStream MCP in Pydantic AI

Get type-safe GetStream interactions in your Pydantic AI agent applications.

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Connect GetStream MCP to Pydantic AI

Create your Vinkius account to connect GetStream 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 GetStream with Pydantic AI

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Type-safe feed management in Pydantic AI

Every response from `get_activities` is validated against your Pydantic models. This prevents silent data corruption if the API structure changes unexpectedly. Your agent uses `partial_update_activity` with strictly typed schemas. If the data doesn't match your requirements, the agent halts immediately, protecting your feed integrity.

Strict collection handling in Pydantic AI

Manage your objects using `batch_get_collections` and `update_collection_object`. Pydantic AI ensures every field in your collection matches your defined types. This approach eliminates hallucinated fields in your data records. You get reliable, predictable state management for all your stored collections.

Validated media and file operations in Pydantic AI

Upload files with `upload_file` and rely on your agent to validate the response schema. This ensures your media metadata remains consistent across your application. Your agent uses `get_open_graph` to pull data, which is then validated before it ever reaches your database. This keeps your feed content clean and reliable.

Setup guide

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

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

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

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Common questions about GetStream MCP in Pydantic AI

The MCPToolset validates every tool response at runtime. If the GetStream API returns unexpected data, your agent throws a validation error instead of processing bad input.

Yes. The tools map directly to your Pydantic schemas, providing a type-safe interface for all 23 feed and collection operations.

You do. The MCP Server must be running externally, and you connect your agent using the MCPToolset with your specific HTTP or SSE transport.

Certainly. Because the toolset is model-agnostic, you can swap between OpenAI, Anthropic, or Gemini while keeping your GetStream logic identical.

The server uses a zero-trust architecture. It only handles the specific activity and collection data payloads you define, and all operations are restricted by your endpoint token.

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