How to Use the Math Evaluation Engine MCP in Pydantic AI

Q: How do I configure the Math Evaluation Engine in Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your HTTP URL. Pass it to the toolsets array when building your Agent.

Eliminate silent math corruption. Add deterministic math to your Pydantic AI agents with this MCP Server.

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Connect Math Evaluation Engine MCP to Pydantic AI

Create your Vinkius account to connect Math Evaluation 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 Math Evaluation Engine with Pydantic AI

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Claude

Perplexity

Type-Safe Math Evaluation Engine

The `calculate_expression` tool guarantees your agent receives an actual float, not a hallucinated string masquerading as a number. Your Pydantic AI agent routes the raw formula string to the MCP Server. The engine computes the exact result using mathjs. When the response comes back, Pydantic AI validates the output against your strict data models. If the agent tries to pass an invalid expression, the failure happens loudly at runtime. You don't get silent corruption in your database.

Deterministic Rounding Validation

The `round_value` tool forces strict decimal limits on floating-point numbers. Your agent sends a raw float and the target decimal count. The tool returns the exact rounded value. This becomes critical when your Pydantic schemas demand strict currency formats. Instead of writing custom validators to catch LLM rounding errors, you offload the operation to this specific tool. The agent gets the exact format required by your models.

Unified Toolset Registration

You register both `calculate_expression` and `round_value` using the new `MCPToolset` class, leaving the deprecated `MCPServerHTTP` behind. You pass the server URL directly to the toolset and attach it to your Agent constructor. This setup doesn't care whether you run OpenAI or local models under the hood. The MCP standard ensures the math tools behave identically across any LLM you plug into your Pydantic AI system.

Setup guide

Set up Math Evaluation 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": {
        "math-evaluation-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Math Evaluation Engine transactions")
print(result.output)

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Common questions about Math Evaluation Engine MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your HTTP URL. Pass it to the `toolsets` array when building your Agent.

Yes. The agent fetches the exact float from the server, which then passes through your Pydantic models. Any type mismatch triggers an immediate validation error.

Python eval is a massive security risk. This engine restricts the grammar to mathjs, giving you exact results without opening a vector for arbitrary code execution.

Yes. Because Pydantic AI is model-agnostic, the math tools function exactly the same whether you use Gemini, Claude, or a local Llama instance.

The engine only ingests plain mathematical strings and raw float values. Vinkius runs the computation in an ephemeral sandbox that destroys itself the moment the connection closes, ensuring zero data persistence.

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