How to Use the Glassnode (On-chain Data) MCP in Pydantic AI

Q: How do I set up the Glassnode (On-chain Data) toolset in Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and initialize an MCPToolset with your Vinkius HTTP endpoint. Pass this toolset directly to your Agent's toolsets parameter to expose the on-chain data tools.

Q: Does this integration support SSE transports for real-time metrics?

Yes, the unified MCPToolset in Pydantic AI supports both Streamable HTTP and SSE transports. This allows your agent to maintain stable, persistent connections to the Vinkius platform for low-latency data fetching.

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Connect Glassnode (On-chain Data) MCP to Pydantic AI

Create your Vinkius account to connect Glassnode (On-chain 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 Glassnode (On-chain Data) with Pydantic AI

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Perplexity

Type-safe metric retrieval for Pydantic AI agents

The `get_metric` tool delivers time-series data directly into your type-safe agent workflows. Because Pydantic AI validates every incoming payload against strict schemas, any deviation in Glassnode's response structure triggers an immediate runtime error instead of failing silently. You register the server using the unified `MCPToolset` class pointing to your Vinkius host. This setup ensures that your agent receives structured, validated floats and timestamps, preventing bad data from corrupting your trading signals.

Strict metadata validation using the MCP Server

The `get_metric_details` tool exposes the exact parameters, intervals, and formats required by specific Glassnode endpoints. Your agent queries this tool to dynamically build validated API calls that match Pydantic models exactly. Using this MCP Server with Pydantic AI guarantees that your agent never attempts to query an unsupported interval. The framework catches schema mismatches locally, saving you API credits and reducing cloud execution costs.

Multi-asset validation loops for production systems

The `get_bulk_metric` tool pulls data for multiple assets in one go, returning structured lists that match your defined Pydantic models. If the server returns unexpected null values or altered token formats, the framework halts execution to protect your downstream systems. This strict validation makes Pydantic AI the ideal choice for production-grade crypto index trackers. You get clean, predictable data streams that your automated systems can act on with absolute certainty.

Setup guide

Set up Glassnode (On-chain 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": {
        "glassnode-on-chain-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Glassnode (On-chain Data) transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Glassnode. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Glassnode (On-chain Data) MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"` and initialize an `MCPToolset` with your Vinkius HTTP endpoint. Pass this toolset directly to your Agent's `toolsets` parameter to expose the on-chain data tools.

Pydantic AI will raise a validation error at runtime, preventing your agent from processing corrupt or malformed data. This strict validation ensures your trading logic never operates on hallucinated or misaligned metrics.

Yes, Pydantic AI is model-agnostic, meaning you can connect these on-chain tools to local models via Ollama or commercial APIs like Anthropic. The type-safety layer works identically regardless of the underlying LLM.

Yes, the unified `MCPToolset` in Pydantic AI supports both Streamable HTTP and SSE transports. This allows your agent to maintain stable, persistent connections to the Vinkius platform for low-latency data fetching.

Your Glassnode API token is injected directly into the ephemeral Vinkius V8 sandbox, isolated from your local runtime. The Pydantic AI client only sends structured JSON RPC requests containing your target asset queries, ensuring your private credentials never touch the client-side code.

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