How to Use the DefiLlama (DeFi Analytics) MCP in Pydantic AI

Q: Does Pydantic AI validate DefiLlama (DeFi Analytics) price charts?

Every point from getpricechart runs through your defined models. The system throws an error if the structure deviates.

Force strict schema validation on every DeFi price and TVL metric with Pydantic AI.

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Connect DefiLlama (DeFi Analytics) MCP to Pydantic AI

Create your Vinkius account to connect DefiLlama (DeFi Analytics) 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 DefiLlama (DeFi Analytics) with Pydantic AI

Ask AI about this MCP

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Perplexity

Type-safe DefiLlama MCP Server integration

`get_current_prices` returns nested JSON that your Pydantic models validate at runtime. Setup requires initializing `MCPToolset` with the HTTP URL, and the framework ensures the agent never hallucinates a missing decimal point. Bad data causes trading errors. If `get_current_tvl_chain` returns an unexpected string instead of a float, the application fails loudly with a validation exception rather than silently corrupting your database.

Parse complex historical arrays

`get_historical_tvl_global` generates massive time-series arrays that demand strict typing. The framework maps these responses directly into predefined Python classes. Evaluating yield requires exact timestamps. `get_pool_historical_data` outputs chronological APY records that the system verifies against your schema before passing anything to the LLM.

Audit protocol volumes

`get_dex_volume_chain` gives your system hard numbers on decentralized exchange activity. The unified MCP toolset approach handles the Streamable HTTP transport automatically. Finding dominant platforms takes accurate metrics. `get_protocols` lists everything on DefiLlama, and Pydantic AI guarantees the resulting TVL fields actually contain numbers.

Setup guide

Set up DefiLlama (DeFi Analytics) 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": {
        "defillama-defi-analytics-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent DefiLlama (DeFi Analytics) transactions")
print(result.output)

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Common questions about DefiLlama (DeFi Analytics) MCP in Pydantic AI

Install the slim package with MCP extras. Pass the server URL to `MCPToolset` and assign it to your agent.

Every point from `get_price_chart` runs through your defined models. The system throws an error if the structure deviates.

The framework is completely model-agnostic. You run these MCP tools against Anthropic, OpenAI, or a local Llama instance.

Scripts crash immediately with a clear validation error. This prevents your system from making decisions on malformed data.

Market cap and DEX volume requests pass through Vinkius infrastructure using ephemeral routing. No persistent connections remain open after your script terminates.

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