How to Use the ComboCurve MCP in Pydantic AI

Build type-safe oil and gas agents by validating ComboCurve data with Pydantic AI.

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Connect ComboCurve MCP to Pydantic AI

Create your Vinkius account to connect ComboCurve 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 ComboCurve with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-Safe Well Data Ingestion

`get_well` pulls asset records directly into your runtime with strict schema validation. If a well API response contains a malformed coordinate or missing ID, the framework catches it instantly. This prevents corrupt data from contaminating your downstream calculations. You get clean, validated Python objects that match your exact data models.

Validated Forecast Extraction via Pydantic AI

This MCP Server exposes `get_monthly_volumes` and `get_forecast` with strict runtime checks. Your agent cannot hallucinate forecast values because every field is validated against your Pydantic models. If the API returns unexpected nested structures, the system fails loudly immediately. This guarantees that your automated economic evaluations run only on verified data.

Project and Scenario Integrity

`list_projects` and `get_scenario_details` retrieve project economics with guaranteed structure. Your agent navigates project trees knowing the exact data types of every field. This model-agnostic approach works whether you run local models or commercial APIs. Your data remains structured and clean throughout the entire analysis pipeline.

Setup guide

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

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

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

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Common questions about ComboCurve MCP in Pydantic AI

Use the toolset constructor with your Vinkius HTTP endpoint. Pass this toolset directly into your agent instance to make all ten tools available.

The framework raises a validation error immediately. This stops the agent from executing decisions based on corrupted or hallucinated well data.

Yes. The framework is model-agnostic, meaning you can run your validated well queries through local LLMs or any major commercial model provider.

No. Vinkius hosts the server externally. Your Python code simply connects to the managed SSE or HTTP endpoint.

Your economic inputs and well logs flow through a zero-trust, ephemeral V8 sandbox. Vinkius handles the API token authentication securely, ensuring your proprietary data never touches a persistent database.

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