Bring On Chain Data
to CrewAI
Learn how to connect Glassnode (On-chain Data) to CrewAI and start using 6 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
What you can do
- Asset Discovery — List all supported assets and blockchains using
list_assetsto identify available data points. - Metric Exploration — Query thousands of metric paths with
list_metricsand get detailed documentation on parameters viaget_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_metricto compare market trends. - Point-in-Time Data — Access immutable historical snapshots via
get_pit_metricto eliminate look-ahead bias in backtesting.
How it works
- Subscribe to this server
- Enter your Glassnode API Key
- Start querying on-chain intelligence from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Crypto Traders — monitor exchange inflows and whale movements without leaving the chat
- Data Scientists — pull clean time-series data directly into your analysis environment
- Financial Analysts — generate reports on network growth and valuation metrics instantly
Built-in capabilities (6)
Use a="*" for all assets. Get bulk metric data for multiple assets
Path should be the metric name like "addresses/active_count" or "market/price_usd_close". Get time-series data for a specific metric
Get details, allowed parameters, and description for a specific metric
Get Point-in-Time (PIT) metric data
List all supported assets on Glassnode
Can be filtered by asset, interval, etc. List all available metric paths on Glassnode
Why CrewAI?
When paired with CrewAI, Glassnode (On-chain Data) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Glassnode (On-chain Data) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
- —
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter and agents auto-discover every available tool at runtime - —
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
- —
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Glassnode (On-chain Data) in CrewAI
Glassnode (On-chain Data) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Glassnode (On-chain Data) to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Glassnode (On-chain Data) in CrewAI
The Glassnode (On-chain Data) 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. All 6 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Glassnode (On-chain Data) for CrewAI
Every tool call from CrewAI to the Glassnode (On-chain Data) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I find the exact path for a specific metric like 'Active Addresses'?
Use the list_metrics tool with the asset symbol (e.g., 'BTC') to see all available paths, or use get_metric_details with a known path to see its full documentation and allowed parameters.
Can I fetch data for multiple coins at once?
Yes, use the get_bulk_metric tool. You can specify a specific asset or use a wildcard * to get data for all supported assets for a specific metric path in a single response.
What is the difference between standard metrics and Point-in-Time (PIT) metrics?
Standard get_metric returns the most accurate current data (which may include revisions). get_pit_metric provides an immutable view of data as it was known at a specific moment, which is essential for backtesting to avoid look-ahead bias.
How does CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
Can different agents in the same crew use different MCP servers?
Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
What happens when an MCP tool call fails during a crew run?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
Can I run CrewAI crews on a schedule (cron)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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