How to Use the CoinLore MCP in Pydantic AI

Use CoinLore with Pydantic AI for type-safe crypto data that fails fast if the API returns garbage.

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Connect CoinLore MCP to Pydantic AI

Create your Vinkius account to connect CoinLore 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 CoinLore with Pydantic AI

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Type-safe crypto data for Pydantic AI

Every call to `get_ticker` or `list_tickers` gets validated against your Pydantic models. If the data format drifts, your agent stops immediately instead of hallucinating values. This keeps your Pydantic AI agent stable. You define the structure you expect, and the MCP Server enforces it at runtime.

History logs in Pydantic AI

Pull annual price trends with `get_ohlcv` and validate them before processing. Your agent relies on strict types to ensure the math it performs on the data is sound. Because you use Pydantic AI, you never have to worry about silent corruption. If the data isn't perfect, the tool call fails, and your agent handles the error gracefully.

Manage exchanges with Pydantic AI

Your agent lists exchanges using `list_exchanges` and validates the returned objects. It ensures every piece of metadata conforms to your application's requirements. This setup works across any model you plug into Pydantic AI. You get the same type-safe experience whether you are running locally or using a cloud provider.

Setup guide

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

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

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

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Common questions about CoinLore MCP in Pydantic AI

You install the slim package with MCP support. Use the MCPToolset class to connect to your server, then pass that toolset to your agent instance.

It's likely a validation error. Pydantic AI is doing its job by stopping the agent when the data doesn't match your expected model structure.

Yes, you can use SSE or Streamable HTTP. Just make sure your server is running and reachable before initializing the toolset.

Yes, the framework is model-agnostic. You can pair it with local models as long as you define your Pydantic schemas correctly.

The server runs in an ephemeral V8 sandbox. We only ever touch public market price and volume data, keeping your private environment completely isolated from the exchange stats.

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