Glassnode (On-chain Data) MCP Server for LangChainGive LangChain 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

LangChain is the leading Python framework for composable LLM applications. Connect Glassnode (On-chain Data) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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The Glassnode (On-chain Data) MCP Server for LangChain 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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VS Code

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+ other MCP clients

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "glassnode-on-chain-data": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Glassnode (On-chain Data), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

LangChain's ecosystem of 500+ components combines seamlessly with Glassnode (On-chain Data) through native MCP adapters. Connect 6 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain

When LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Glassnode (On-chain Data) via MCP

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

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

The largest ecosystem of integrations, chains, and agents. combine Glassnode (On-chain Data) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Glassnode (On-chain Data) queries for multi-turn workflows

Glassnode (On-chain Data) + LangChain Use Cases

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

RAG with live data: combine Glassnode (On-chain Data) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Glassnode (On-chain Data), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Glassnode (On-chain Data) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Glassnode (On-chain Data) tool call, measure latency, and optimize your agent's performance

Example Prompts for Glassnode (On-chain Data) in LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Glassnode (On-chain Data) + LangChain FAQ

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

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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