How to Use the CoinMarketCap MCP in Pydantic AI

Strictly typed CoinMarketCap integration for Pydantic AI agents.

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Connect CoinMarketCap MCP to Pydantic AI

Create your Vinkius account to connect CoinMarketCap 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 CoinMarketCap with Pydantic AI

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Strictly typed market quotes in Pydantic AI

Every response from `get_quotes` is validated against your Pydantic models at runtime. If the API returns a malformed price, the agent fails immediately. This ensures your Pydantic AI agent never works with garbage data. It forces the system to stay within the expected schema, preventing silent errors during complex calculations.

Global metrics for Pydantic AI

Pull aggregate market health stats using `get_global_metrics` to set the baseline for your agent. It gets the full picture of the crypto market in one call. Since Pydantic AI enforces types, you can be sure the returned integers and floats map perfectly to your code. It makes the data ready for immediate use in your logic.

Safe conversions in Pydantic AI

Use `price_conversion` to calculate exchange rates with full type safety. It prevents the agent from hallucinating fields or corrupting currency values. Your agent remains reliable because the tool output undergoes rigorous validation before it ever touches your core logic. If the data isn't perfect, the agent knows it.

Setup guide

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

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

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

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Common questions about CoinMarketCap MCP in Pydantic AI

Install the slim MCP package and use the MCPToolset to link your agent. It works with any model you choose, as long as your types match.

The agent will raise a validation error immediately. This stops the process before bad data can cause downstream issues.

Yes, the toolset supports both Streamable HTTP and SSE transports. You can pick the one that fits your deployment architecture.

Absolutely. Because the validation happens at the toolset level, it doesn't matter if you use OpenAI, Anthropic, or local models.

Data privacy is handled by the ephemeral nature of the server connection. Only validated, structured JSON responses are passed back to your agent, ensuring no unverified data enters your code.

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