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How to Use the GetBlock (Web3 RPC Provider) MCP in Pydantic AI

Build type-safe Web3 agents with Pydantic AI that validate every RPC response at runtime to prevent corrupted state.

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Connect GetBlock (Web3 RPC Provider) MCP to Pydantic AI

Create your Vinkius account to connect GetBlock (Web3 RPC Provider) 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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Type-Safe Web3 Queries with Pydantic AI

Stop worrying about silent failures or unexpected chain API responses breaking your Pydantic AI validation pipeline. When your agent calls `eth_get_balance` or `sol_get_balance`, the framework validates the returned JSON-RPC payloads against strict schemas. If a field is missing or malformed, the agent fails immediately instead of passing corrupted data to your business logic. The MCP Server ensures that responses from Bitcoin, Ethereum, and Solana are structured consistently, allowing your Pydantic AI agent to make decisions based on verified data.

Validate Transaction Payloads Before Broadcasting

Building transactions with LLMs is notoriously risky due to hallucinations. By using `btc_createpsbt` and `btc_analyzepsbt`, your Pydantic AI agent can construct and inspect transaction structures with strict validation. The framework ensures that the output conforms to your exact Pydantic models before you sign and broadcast. Once validated, the agent can safely submit the payload using `btc_sendrawtransaction` or `sol_send_transaction`. This runtime verification prevents common errors like incorrect gas estimation or malformed transaction hexes from reaching the mempool.

Debug EVM Execution with Runtime Validation

Inspecting complex smart contract states requires reliable data structures. Your agent can use `debug_trace_call` and `eth_get_code` to analyze execution paths during dry runs. Pydantic's validation layer parses the trace outputs, ensuring your agent can reliably identify revert reasons and gas consumption. This approach eliminates the guesswork from debugging smart contracts. If the RPC node returns an unexpected trace format, the schema validation catches it instantly, protecting your agent from interpreting corrupted debug data.

Setup guide

Set up GetBlock (Web3 RPC Provider) MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "getblock-web3-rpc-provider-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to GetBlock (Web3 RPC Provider) tools.",
)

result = await agent.run("List recent GetBlock (Web3 RPC Provider) transactions")
print(result.output)

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Common questions about GetBlock (Web3 RPC Provider) MCP in Pydantic AI

Install the slim package with MCP support, then use `MCPToolset` with your Vinkius HTTP URL. Pass this toolset directly into your `Agent` constructor to give your agent access to the 26 RPC tools.
Yes, this server supports both Streamable HTTP and SSE transports. You can configure the `MCPToolset` to use either transport method depending on your network topology and performance requirements.
If an RPC call like `sol_get_token_account_balance` returns data that doesn't match the expected schema, the framework raises a validation error. This prevents the agent from continuing with hallucinated or corrupted balance figures.
Yes, the framework is model-agnostic. You can connect your MCP Server to agents running on local models, OpenAI, or Anthropic, while maintaining the same strict runtime validation for all RPC calls.
This server operates as a stateless proxy. It does not store wallet addresses, transaction hashes, or RPC payloads. Every request to check a balance via `eth_get_balance` is processed in an ephemeral sandbox and discarded immediately after completion.

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