How to Use the Lineascan MCP in Pydantic AI

Build type-safe blockchain agents with Pydantic AI. Validate every Linea data point against your models for absolute reliability.

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Connect Lineascan MCP to Pydantic AI

Create your Vinkius account to connect Lineascan 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 Lineascan with Pydantic AI

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Type-Safe Chain Queries

Pydantic AI validates every response from `get_balance` or `eth_price` against your defined schemas. You catch data mismatches before they reach your logic. This prevents silent failures when the chain state changes. Your agent only proceeds if the data matches your strict type requirements.

Transaction History Validation

Track `tx_list` and `tx_list_internal` results with full schema enforcement. Your agent processes blockchain history without the risk of hallucinated fields. Define your models to match the structure of `token_tx` outputs. Pydantic AI ensures the data is correct or throws an error immediately.

Contract Verification and Auditing

Retrieve `get_abi` and `get_source_code` payloads and validate them against your expected contract structures. Your agent audits contracts with guaranteed data integrity. Use `check_verify_status` to confirm that your contract verification requests succeeded. The tool output maps directly to your validated model fields.

Setup guide

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

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

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

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Common questions about Lineascan MCP in Pydantic AI

Use the MCPToolset provided by the pydantic-ai-slim package. Connect to your Vinkius endpoint to start using the 25 available tools.

Yes, Pydantic AI validates the server response at runtime. If the data doesn't match your schema, the agent stops before any action is taken.

Absolutely. Since Pydantic AI is model-agnostic, the tools work the same regardless of whether you use OpenAI, Anthropic, or local models.

It is designed for systems where correctness is the priority. The combination of MCP and strict typing ensures your agent behaves as expected.

It only touches public blockchain data like block headers and transaction receipts. Vinkius ensures the connection remains isolated and secure.

Use it with your favorite AI tools

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