Glassnode (On-chain Data) MCP Server for Pydantic AIGive Pydantic AI instant access to 6 tools to Get Bulk Metric, Get Metric, Get Metric Details, and more

Name: Glassnode (On-chain Data)
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Glassnode (On-chain Data) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Glassnode (On-chain Data) MCP Server for Pydantic AI is a standout in the Data Analytics category — giving your AI agent 6 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Glassnode (On-chain Data) "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Glassnode (On-chain Data)?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Glassnode (On-chain Data) MCP Server

Connect your Glassnode account to any AI agent to analyze crypto markets with precision. Fetch real-time and historical on-chain metrics, exchange flows, and network health data through natural conversation.

Pydantic AI validates every Glassnode (On-chain Data) tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Asset Discovery — List all supported assets and blockchains using list_assets to identify available data points.
Metric Exploration — Query thousands of metric paths with list_metrics and get detailed documentation on parameters via get_metric_details.
Time-Series Analysis — Retrieve historical data for active addresses, exchange balances, and price metrics using get_metric.
Bulk Data — Fetch metrics for multiple assets simultaneously with get_bulk_metric to compare market trends.
Point-in-Time Data — Access immutable historical snapshots via get_pit_metric to eliminate look-ahead bias in backtesting.

The Glassnode (On-chain Data) MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 6 Glassnode (On-chain Data) tools available for Pydantic AI

When Pydantic AI connects to Glassnode (On-chain Data) through Vinkius, your AI agent gets direct access to every tool listed below — spanning on-chain-data, market-intelligence, crypto-analytics, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

get

Get bulk metric on Glassnode (On-chain Data)

Use a="*" for all assets. Get bulk metric data for multiple assets

get

Get metric on Glassnode (On-chain Data)

Path should be the metric name like "addresses/active_count" or "market/price_usd_close". Get time-series data for a specific metric

get

Get metric details on Glassnode (On-chain Data)

Get details, allowed parameters, and description for a specific metric

get

Get pit metric on Glassnode (On-chain Data)

Get Point-in-Time (PIT) metric data

list

List assets on Glassnode (On-chain Data)

List all supported assets on Glassnode

list

List metrics on Glassnode (On-chain Data)

Can be filtered by asset, interval, etc. List all available metric paths on Glassnode

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

Follow these steps to wire Glassnode (On-chain Data) into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Glassnode (On-chain Data) with type-safe schemas

Why Use Pydantic AI with the Glassnode (On-chain Data) MCP Server

Pydantic AI provides unique advantages when paired with Glassnode (On-chain Data) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Glassnode (On-chain Data) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Glassnode (On-chain Data) connection logic from agent behavior for testable, maintainable code

Glassnode (On-chain Data) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Glassnode (On-chain Data) MCP Server delivers measurable value.

Type-safe data pipelines: query Glassnode (On-chain Data) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Glassnode (On-chain Data) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Glassnode (On-chain Data) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Glassnode (On-chain Data) responses and write comprehensive agent tests

Example Prompts for Glassnode (On-chain Data) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Glassnode (On-chain Data) immediately.

"List all supported assets on Glassnode."

"Get the 'addresses/active_count' metric for BTC from the last 7 days with a 24h interval."

"Show me the details and allowed parameters for the metric path 'market/price_usd_close'."

Troubleshooting Glassnode (On-chain Data) MCP Server with Pydantic AI

Common issues when connecting Glassnode (On-chain Data) to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Glassnode (On-chain Data) + Pydantic AI FAQ

Common questions about integrating Glassnode (On-chain Data) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Glassnode (On-chain Data) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.