How to Use the Nearblocks (Near Blockchain Explorer API) MCP in Pydantic AI

Q: What is the setup process for Nearblocks (Near Blockchain Explorer API) with Pydantic AI?

After installing the required packages, you create an MCPToolset instance pointing to the server URL. Then you simply pass that toolset into your Pydantic AI Agent's constructor.

Query the NEAR blockchain with Pydantic AI and get type-safe, validated data structures back in your Python code.

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Connect Nearblocks (Near Blockchain Explorer API) MCP to Pydantic AI

Create your Vinkius account to connect Nearblocks (Near Blockchain Explorer API) 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 Nearblocks (Near Blockchain Explorer API) with Pydantic AI

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Get Type-Safe Blockchain Data

This server lets your agent call 11 different functions to get data from the NEAR blockchain. You can ask for transaction details with `get_transaction_details` or token information with `get_token_details`. Here's the important part: Pydantic AI automatically validates the response against a Pydantic model. If the API ever returns malformed data or an unexpected field, your code will raise a `ValidationError` immediately. No more silent data corruption.

Build Reliable On-Chain Automations

When you're building an agent that acts on blockchain data, correctness is everything. Use `get_account_tokens` to check a balance or `get_account_transactions` to verify a recent transfer. Because every response is validated, you can trust the data your agent is working with. This gives you the confidence to build automations that depend on accurate on-chain state, without worrying about weird edge cases from the API.

A Model-Agnostic NEAR MCP Server

The Pydantic AI framework is model-agnostic, and so is this MCP server. You can use it with OpenAI, Anthropic, Gemini, or even a local model you're running yourself. Just add the `MCPToolset` to your Pydantic AI agent. It doesn't matter which LLM is powering it. Your agent will get the same set of reliable, validated tools for querying the NEAR network, like `get_latest_blocks` and `get_network_stats`.

Setup guide

Set up Nearblocks (Near Blockchain Explorer API) 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": {
        "nearblocks-near-blockchain-explorer-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Nearblocks (Near Blockchain Explorer API) transactions")
print(result.output)

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Common questions about Nearblocks (Near Blockchain Explorer API) MCP in Pydantic AI

Pydantic AI automatically parses the JSON response from tools like `get_transaction_details` into Pydantic models. If the data doesn't match the expected schema, it fails loudly with an error, ensuring type safety.

Yes. Pydantic AI is model-agnostic. You can connect this MCP server to an agent powered by OpenAI, Gemini, Claude, or a local model, and it will work the same way.

After installing the required packages, you create an `MCPToolset` instance pointing to the server URL. Then you simply pass that toolset into your Pydantic AI `Agent`'s constructor.

Your agent won't get bad data. Pydantic AI's validation layer will catch any deviation from the expected data structure and raise an exception. This prevents your agent from making decisions based on corrupted or incomplete information.

The server is stateless and only ever sees requests for public on-chain data, like a specific account's NFT inventory. Vinkius provides a sandboxed environment that isolates the connection, so your application code and LLM provider keys remain private.

Use it with your favorite AI tools

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