How to Use the TokenTerminal (Crypto Financial Data) MCP in Pydantic AI

Q: Can I get historical time series metrics using TokenTerminal (Crypto Financial Data) with Pydantic AI?

Yes, you can retrieve these time series metrics via getprojectmetrics. Since the response is modeled, you know exactly what fields—like date and value—to expect every single time.

Q: Is TokenTerminal (Crypto Financial Data) compatible with Pydantic AI?

It's model-agnostic. Because it validates the output rather than the underlying LLM, you can use this server with OpenAI, Anthropic, or Gemini without worrying about data structure changes.

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Connect TokenTerminal (Crypto Financial Data) MCP to Pydantic AI

Create your Vinkius account to connect TokenTerminal (Crypto Financial Data) 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 TokenTerminal (Crypto Financial Data) with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Ensuring Metric Accuracy

When you query metrics with `get_project_metrics`, the results are immediately validated against your defined Pydantic models. This means if the API sends garbage data, your agent fails loudly instead of silently corrupting your workflow.

Listing and Validating Projects

The `list_projects` tool provides raw asset lists, but Pydantic AI gives you confidence in that list. Every project name and associated metadata is type-checked at runtime before the agent moves forward.

Gathering Market Data

Using `get_market_metrics` doesn't mean accepting unvalidated fields. The framework ensures every piece of aggregated data—from metrics to totals—conforms exactly to your expected Python structure.

Setup guide

Set up TokenTerminal (Crypto Financial Data) 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": {
        "tokenterminal-crypto-financial-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent TokenTerminal (Crypto Financial Data) transactions")
print(result.output)

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Common questions about TokenTerminal (Crypto Financial Data) MCP in Pydantic AI

Pydantic enforces type safety on every response. When the agent gets crypto financial data, it runs through a validation layer first. This stops bad data from ever reaching your application logic.

Yes, you can retrieve these time series metrics via `get_project_metrics`. Since the response is modeled, you know exactly what fields—like date and value—to expect every single time.

It's model-agnostic. Because it validates the *output* rather than the underlying LLM, you can use this server with OpenAI, Anthropic, or Gemini without worrying about data structure changes.

It delivers institutional-grade crypto financial data. This includes project metadata, market metrics, revenue figures, and active user counts.

The system provides runtime validation. If any tool returns unexpected data—say, a missing required field for 'revenue'—the agent halts immediately with an explicit validation error.

Use it with your favorite AI tools

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