query

name	query
description	Query Ethereum network data via ethpandaops CLI or MCP server. Use when analyzing blockchain data, block timing, attestations, validator performance, network health, or infrastructure metrics. Provides access to ClickHouse (blockchain data), Prometheus (metrics), Loki (logs), and Dora (explorer APIs). Use when this capability is needed.
metadata	{"author":"ethpandaops"}

ethpandaops Query Guide

Query Ethereum network data through the ethpandaops tools. Execute Python code in sandboxed containers with access to ClickHouse blockchain data, Prometheus metrics, Loki logs, and Dora explorer APIs.

Workflow

1. Discover - Find available datasources and schemas
2. Find patterns - Search for query examples and runbooks
3. Execute - Run Python using the ethpandaops library

Access Methods

This skill works with either the CLI (panda binary) or the MCP server. Prefer the CLI — it is always available. Only use the MCP tools (execute_python, manage_session, search) if they appear in your available tools list. If they do not, use the CLI equivalents below via the Bash tool.

CLI (panda binary) — primary interface

# Discovery
panda datasources                          # List all datasources
panda datasources --type clickhouse        # Filter by type
panda schema                               # List ClickHouse tables
panda schema beacon_api_eth_v1_events_block  # Show table schema
panda docs                                 # List Python API modules
panda docs clickhouse                      # Show module docs

# Search
panda search examples "block arrival time"
panda search examples "attestation" --category attestations --limit 5
panda search runbooks "finality delay"
panda search runbooks "validator" --tag performance

# Execute
panda execute --code 'from ethpandaops import clickhouse; print(clickhouse.list_datasources())'
panda execute --file script.py
panda execute --code '...' --session <id>  # Reuse session
echo 'print("hello")' | panda execute

# Sessions
panda session list
panda session create
panda session destroy <session-id>

All commands support --json for structured output.

MCP Server (when available as plugin)

Resource	Description
datasources://list	All configured datasources
datasources://clickhouse	ClickHouse clusters
datasources://prometheus	Prometheus instances
datasources://loki	Loki instances
networks://active	Active Ethereum networks
clickhouse://tables	Available tables
clickhouse://tables/{table}	Table schema details
python://ethpandaops	Python library API docs

search_examples(query="block arrival time")
search_runbooks(query="network not finalizing")
execute_python(code="...")
manage_session(operation="list")

The ethpandaops Python Library

ClickHouse - Blockchain Data

from ethpandaops import clickhouse

# List available clusters
clusters = clickhouse.list_datasources()
# Returns: [{"name": "xatu", "database": "default"}, {"name": "xatu-cbt", ...}]

# Query data (returns pandas DataFrame)
df = clickhouse.query("xatu-cbt", """
    SELECT
        slot,
        avg(seen_slot_start_diff) as avg_arrival_ms
    FROM mainnet.fct_block_first_seen_by_node
    WHERE slot_start_date_time >= now() - INTERVAL 1 HOUR
    GROUP BY slot
    ORDER BY slot DESC
""")

# Parameterized queries
df = clickhouse.query("xatu", "SELECT * FROM blocks WHERE slot > {slot}", {"slot": 1000})

Cluster selection:

• xatu-cbt - Pre-aggregated tables (faster, use for metrics)
• xatu - Raw event data (use for detailed analysis)

Required filters:

• ALWAYS filter on partition key: slot_start_date_time >= now() - INTERVAL X HOUR
• Filter by network: meta_network_name = 'mainnet' or use schema like mainnet.table_name

Prometheus - Infrastructure Metrics

from ethpandaops import prometheus

# List instances
instances = prometheus.list_datasources()

# Instant query
result = prometheus.query("ethpandaops", "up")

# Range query
result = prometheus.query_range(
    "ethpandaops",
    "rate(http_requests_total[5m])",
    start="now-1h",
    end="now",
    step="1m"
)

Time formats: RFC3339 or relative (now, now-1h, now-30m)

Loki - Log Data

Always discover labels first. Before querying logs, fetch the available labels and their values so you can add the right filters. Unfiltered Loki queries are slow and may time out — label filters narrow the search at the storage level and are essential for efficient log retrieval.

from ethpandaops import loki

# Step 1: List instances
instances = loki.list_datasources()

# Step 2: Fetch all available labels
labels = loki.get_labels("ethpandaops")
print(labels)
# Example: ['app', 'cluster', 'ethereum_cl', 'ethereum_el', 'ethereum_network',
#           'instance', 'namespace', 'node', 'testnet', 'validator_client', ...]

# Step 3: Get values for a specific label to build your filter
networks = loki.get_label_values("ethpandaops", "testnet")
print(networks)  # e.g. ['fusaka-devnet-3', 'hoodi', 'sepolia', ...]

cl_clients = loki.get_label_values("ethpandaops", "ethereum_cl")
print(cl_clients)  # e.g. ['lighthouse', 'prysm', 'teku', 'nimbus', 'lodestar', 'grandine']

# Step 4: Query logs with label filters
logs = loki.query(
    "ethpandaops",
    '{testnet="hoodi", ethereum_cl="lighthouse"} |= "error"',
    start="now-1h",
    limit=100
)

Key labels for Ethereum log queries:

• testnet — network/devnet name (e.g. hoodi, fusaka-devnet-3)
• ethereum_cl — consensus layer client (e.g. lighthouse, prysm, teku)
• ethereum_el — execution layer client (e.g. geth, nethermind, besu)
• ethereum_network — Ethereum network name
• instance — specific node instance
• validator_client — validator client name

Log level formats vary by client. When filtering logs by severity, be aware that Ethereum clients format log levels differently:

• Keywords: CRIT, ERR, ERROR, WARN, INFO, DEBUG
• Structured fields: level=error, "level":"error", "severity":"ERROR"
• Shorthand: E, W, C

Start with |~ "(?i)(CRIT|ERR)" as a default filter. If it returns no results, fetch a few unfiltered log lines to identify the client's format, then adapt the regex (e.g. |~ "level=(error|fatal)").

Dora - Beacon Chain Explorer

Discovering all Dora API endpoints:

Before using Dora, discover the full set of available API endpoints by fetching the Swagger documentation. The swagger page is always at <dora-url>/api/swagger/index.html.

1. First, get the Dora base URL for the network:

from ethpandaops import dora
base_url = dora.get_base_url("mainnet")
print(f"Swagger docs: {base_url}/api/swagger/index.html")

2. Then use WebFetch to read the swagger page at {base_url}/api/swagger/index.html to discover all supported API endpoints for that Dora instance. This is important because different Dora deployments may support different endpoints.

3. Use the discovered endpoints to make targeted API calls via the Python dora module or direct HTTP requests.

Use search(type="examples", query="network overview") and search(type="examples", query="dora") for common API patterns.

Direct HTTP calls for endpoints not in the Python module:

from ethpandaops import dora
import httpx

base_url = dora.get_base_url("mainnet")
# Call any endpoint discovered from swagger
with httpx.Client(timeout=30) as client:
    resp = client.get(f"{base_url}/api/v1/<endpoint>")
    data = resp.json()

Storage - Upload Outputs

from ethpandaops import storage

# Save visualization
import matplotlib.pyplot as plt
plt.savefig("/workspace/chart.png")

# Upload for public URL
url = storage.upload("/workspace/chart.png")
print(f"Chart URL: {url}")

# List uploaded files
files = storage.list_files()

Session Management

Critical: Each execution runs in a fresh Python process. Variables do NOT persist.

Files persist: Save to /workspace/ to share data between calls.

Reuse sessions: Pass --session <id> (CLI) or session_id (MCP) for faster startup and workspace persistence.

Multi-Step Analysis Pattern

# Call 1: Query and save
from ethpandaops import clickhouse
df = clickhouse.query("xatu-cbt", "SELECT ...")
df.to_parquet("/workspace/data.parquet")

# Call 2: Load and visualize (reuse session from Call 1)
import pandas as pd
import matplotlib.pyplot as plt
from ethpandaops import storage

df = pd.read_parquet("/workspace/data.parquet")
plt.figure(figsize=(12, 6))
plt.plot(df["slot"], df["value"])
plt.savefig("/workspace/chart.png")
url = storage.upload("/workspace/chart.png")
print(f"Chart: {url}")

Error Handling

ClickHouse errors include actionable suggestions:

• Missing date filter → "Add slot_start_date_time >= now() - INTERVAL X HOUR"
• Wrong cluster → "Use xatu-cbt for aggregated metrics"
• Query timeout → Break into smaller time windows

Default execution timeout is 60s, max 600s. For large analyses:

• Search for optimized patterns first (panda search examples "...")
• Break work into smaller time windows
• Save intermediate results to /workspace/

Notes

• Always filter ClickHouse queries on partition keys (slot_start_date_time)
• Use xatu-cbt for pre-aggregated metrics, xatu for raw event data
• Use panda docs or python://ethpandaops resource for complete API documentation
• Search for examples before writing complex queries from scratch
• Search for runbooks to find common investigation workflows
• Upload visualizations with storage.upload() for shareable URLs
• NEVER just copy/paste/recite base64 of images. You MUST save the image to the workspace and upload it to give it back to the user.

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