How to Use the Moving Average Engine MCP in Pydantic AI

Q: How do I link the Moving Average Engine to Pydantic AI?

Install the pydantic-ai-slimmcp package. Initialize an MCPToolset with your HTTP endpoint and pass it into the toolsets parameter of your Agent.

Enforce strict type-safe moving average calculations in your Pydantic AI pipelines.

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Connect Moving Average Engine MCP to Pydantic AI

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Setup Moving Average Engine with Pydantic AI

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Type-safe financial math

The `calculate_moving_average` tool executes precise Simple (SMA) and Exponential (EMA) math on arrays of numbers. Pydantic AI demands strict runtime validation for every action. When your agent calls this MCP tool, the framework verifies the input parameters against a hardcoded schema before the network request even fires. If the underlying model tries to send a string instead of a float array, the framework fails loudly. You catch the error immediately. This prevents silent data corruption from ruining your downstream trading logic.

Model-agnostic MCP Server

The `calculate_moving_average` tool works identically whether your Pydantic AI agent uses Anthropic, OpenAI, or a local open-source model. You configure the connection using the unified `MCPToolset` approach. The engine exposes standard JSON schemas that any capable model can read. We built this for developers who care about correctness over speed. You pass the initialized toolset to your Agent constructor. The external server handles the heavy lifting, ensuring your local application footprint remains tiny while delivering exact quantitative results.

Stop hallucinated indicators

The `calculate_moving_average` tool completely removes the LLM from the actual arithmetic process. Autoregressive models cannot accurately calculate an EMA because the formula requires recursive historical weighting. This tool forces the agent to rely on a deterministic calculator. You feed raw price arrays into the system. The agent decides the period length based on the user prompt, formats the request, and waits for the exact answer. You get mathematical certainty wrapped in strict Python typing.

Setup guide

Set up Moving Average Engine 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": {
        "moving-average-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Moving Average Engine transactions")
print(result.output)

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Common questions about Moving Average Engine MCP in Pydantic AI

Install the `pydantic-ai-slim[mcp]` package. Initialize an `MCPToolset` with your HTTP endpoint and pass it into the `toolsets` parameter of your Agent.

Yes. The framework validates the agent's proposed arguments against the tool's expected schema. If the model hallucinates a parameter, the system throws a validation error instantly.

Yes. The framework is model-agnostic. Any local model capable of tool calling can interact with the external HTTP server to get exact calculations.

The server will return an error stating the data points are insufficient for the requested moving average period. Your agent must supply an array at least as long as the period length.

We enforce a zero-trust model where every calculation runs in an isolated, temporary container. The memory space containing your numerical arrays is wiped the millisecond the HTTP response sends. Auth requires a single endpoint token, and no logs retain your numbers.

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