How to Use the MACD & RSI Oscillator Engine MCP in Pydantic AI

Q: How do I configure the MACD & RSI Oscillator Engine with Pydantic AI?

Run pip install "pydantic-ai-slimmcp". Create an MCPToolset with your HTTP MCP endpoint and pass it to your agent's toolsets list.

Q: Why did Pydantic AI deprecate MCPServerHTTP for the MACD & RSI Oscillator Engine MCP server?

The framework moved to the unified MCPToolset approach. It simplifies the connection logic for Streamable HTTP and SSE transports.

Type-safe technical analysis with Pydantic AI and the MACD & RSI Oscillator Engine.

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Connect MACD & RSI Oscillator Engine MCP to Pydantic AI

Create your Vinkius account to connect MACD & RSI Oscillator Engine 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 MACD & RSI Oscillator Engine with Pydantic AI

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Perplexity

Type-safe oscillator calculations via MCP Server

The `calculate_macd_rsi` tool requires strict configuration parameters and price arrays. It processes these inputs to compute exact momentum metrics for your trading models. Pydantic AI validates every response at runtime. If the MCP Server returns a string instead of a float for a signal line, the framework throws a validation error immediately. No silent failures.

Model-agnostic quantitative analysis

Executing `calculate_macd_rsi` works across any language model. You pick the language model. Route through OpenAI, Anthropic, or a local Llama instance — the agent interacts with the oscillator engine exactly the same way. The `MCPToolset` standardizes the MCP interface. You configure the HTTP connection once, and your agent gains reliable technical analysis capabilities regardless of the underlying LLM.

Strict inputs for financial data

The `calculate_macd_rsi` tool demands clean price arrays. By defining your expected outputs as Pydantic models, you force the agent to respect your schema. When your agent calls the tool, the framework ensures the data matches the required format. You spend less time debugging hallucinated fields and more time backtesting strategies.

Setup guide

Set up MACD & RSI Oscillator 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": {
        "macd-rsi-oscillator-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent MACD & RSI Oscillator Engine transactions")
print(result.output)

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Common questions about MACD & RSI Oscillator Engine MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"`. Create an `MCPToolset` with your HTTP MCP endpoint and pass it to your agent's `toolsets` list.

The framework moved to the unified `MCPToolset` approach. It simplifies the connection logic for Streamable HTTP and SSE transports.

Yes. The framework is model-agnostic, meaning you wire the tools to local instances just as easily as commercial APIs.

The framework catches it instantly. It validates the response against your Pydantic models and raises a loud error before the bad data corrupts your pipeline.

The engine processes raw price arrays in memory and returns the math. It does not store your proprietary ticker data or transmit it to external logging services.

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