How to Use the Bitquery (Web3 Blockchain GraphQL APIs) MCP in Pydantic AI

Execute type-safe Web3 GraphQL queries with strict runtime validation using Pydantic AI and this MCP Server.

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Connect Bitquery (Web3 Blockchain GraphQL APIs) MCP to Pydantic AI

Create your Vinkius account to connect Bitquery (Web3 Blockchain GraphQL APIs) 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 Bitquery (Web3 Blockchain GraphQL APIs) with Pydantic AI

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Type-Validated Live Blockchain Streaming

`query_v2` runs GraphQL operations against the streaming API to fetch live trades, balance updates, and smart contract instructions. This tool feeds raw JSON data directly into your type-safe agent pipelines. Pydantic AI validates the incoming blockchain payload from the MCP Server at runtime. If a transaction field changes shape, the agent catches the error immediately instead of processing corrupt data.

Runtime-Checked Historical Chain Queries with MCP Server

`query_v1` retrieves historical ledger events and transaction records from over 40 blockchains. This tool executes standard GraphQL queries and returns the structured historical datasets your agent requests. The framework ensures every returned block height, wallet address, and token amount matches your strict Python type hints. This prevents silent failures when parsing complex multi-chain data structures.

Secure Token Lifecycle Management

`generate_token` creates the OAuth2 access token needed to authenticate your queries. This tool configures the BITQUERY_ACCESS_TOKEN variable safely behind the scenes. Because Pydantic AI handles tools through a unified toolset, the token generation process integrates directly into your agent setup. You never have to manually pass tokens to individual query calls.

Setup guide

Set up Bitquery (Web3 Blockchain GraphQL APIs) 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": {
        "bitquery-web3-blockchain-graphql-apis-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Bitquery (Web3 Blockchain GraphQL APIs) tools.",
)

result = await agent.run("List recent Bitquery (Web3 Blockchain GraphQL APIs) transactions")
print(result.output)

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Common questions about Bitquery (Web3 Blockchain GraphQL APIs) MCP in Pydantic AI

Use the MCPToolset constructor with the server URL and pass it to your agent. This single toolset exposes all historical and streaming capabilities.

The framework raises a validation error immediately. This keeps your agent from hallucinating or operating on malformed transaction data.

Yes, the server is model-agnostic. You can use it with local models or commercial APIs while maintaining full type safety.

Yes, this server supports both Streamable HTTP and SSE transports. You just configure the toolset with the corresponding server endpoint.

Your client credentials stay within your private Python environment. The server uses them locally to sign token requests, so your secrets never leak to external logging systems.

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