A Model Completion Protocol (MCP) server for the Cryo blockchain data extraction tool.
Cryo MCP allows you to access Cryo's powerful blockchain data extraction capabilities via an API server that implements the MCP protocol, making it easy to query blockchain data from any MCP-compatible client.
When using this MCP server to run SQL queries on blockchain data, follow this workflow:
Download data with query_dataset:
result = query_dataset(
dataset="blocks", # or "transactions", "logs", etc.
blocks="15000000:15001000", # or use blocks_from_latest=100
output_format="parquet" # important: use parquet for SQL
)
files = result.get("files", []) # Get the returned file paths
Explore schema with get_sql_table_schema:
# Check what columns are available in the file
schema = get_sql_table_schema(files[0])
# Now you can see all columns, data types, and sample data
Run SQL with query_sql:
# Option 1: Simple table reference (DuckDB will match the table name to file)
sql_result = query_sql(
query="SELECT block_number, timestamp, gas_used FROM blocks",
files=files # Pass the files from step 1
)
# Option 2: Using read_parquet() with explicit file path
sql_result = query_sql(
query=f"SELECT block_number, timestamp, gas_used FROM read_parquet('{files[0]}')",
files=files # Pass the files from step 1
)
Alternatively, use the combined approach with query_blockchain_sql:
# Option 1: Simple table reference
result = query_blockchain_sql(
sql_query="SELECT * FROM blocks",
dataset="blocks",
blocks_from_latest=100
)
# Option 2: Using read_parquet()
result = query_blockchain_sql(
sql_query="SELECT * FROM read_parquet('/path/to/file.parquet')", # Path doesn't matter
dataset="blocks",
blocks_from_latest=100
)
For a complete working example, see examples/sql_workflow_example.py.
This is not required if you will run the tool with uvx directly.
# install with UV (recommended)
uv tool install cryo-mcp
claude mcp add for an interactive prompt.uvx as the command to run.cryo-mcp --rpc-url <ETH_RPC_URL> [--data-dir <DATA_DIR>] as the argsETH_RPC_URL and CRYO_DATA_DIR as environment variables instead.New instances of claude will now have access to cryo as configured to hit your RPC endpoint and store data in the specified directory.
Cryo MCP exposes the following MCP tools:
list_datasets()Returns a list of all available Cryo datasets.
Example:
client.list_datasets()
query_dataset()Query a Cryo dataset with various filtering options.
Parameters:
dataset (str): The name of the dataset to query (e.g., 'blocks', 'transactions', 'logs')blocks (str, optional): Block range specification (e.g., '1000:1010')start_block (int, optional): Start block number (alternative to blocks)end_block (int, optional): End block number (alternative to blocks)use_latest (bool, optional): If True, query the latest blockblocks_from_latest (int, optional): Number of blocks from latest to includecontract (str, optional): Contract address to filter byoutput_format (str, optional): Output format ('json', 'csv', 'parquet')include_columns (list, optional): Columns to include alongside defaultsexclude_columns (list, optional): Columns to exclude from defaultsExample:
# Get transactions from blocks 15M to 15.01M
client.query_dataset('transactions', blocks='15M:15.01M')
# Get logs for a specific contract from the latest 100 blocks
client.query_dataset('logs', blocks_from_latest=100, contract='0x1234...')
# Get just the latest block
client.query_dataset('blocks', use_latest=True)
lookup_dataset()Get detailed information about a specific dataset, including schema and sample data.
Parameters:
name (str): The name of the dataset to look upsample_start_block (int, optional): Start block for sample datasample_end_block (int, optional): End block for sample datause_latest_sample (bool, optional): Use latest block for samplesample_blocks_from_latest (int, optional): Number of blocks from latest for sampleExample:
client.lookup_dataset('logs')
get_latest_ethereum_block()Returns information about the latest Ethereum block.
Example:
client.get_latest_ethereum_block()
Cryo MCP includes several tools for running SQL queries against blockchain data:
query_sql()Run a SQL query against downloaded blockchain data.
Parameters:
query (str): SQL query to executefiles (list, optional): List of parquet file paths to query. If None, will use all files in the data directory.include_schema (bool, optional): Whether to include schema information in the resultExample:
# Run against all available files
client.query_sql("SELECT * FROM read_parquet('/path/to/blocks.parquet') LIMIT 10")
# Run against specific files
client.query_sql(
"SELECT * FROM read_parquet('/path/to/blocks.parquet') LIMIT 10",
files=['/path/to/blocks.parquet']
)
query_blockchain_sql()Query blockchain data using SQL, automatically downloading any required data.
Parameters:
sql_query (str): SQL query to executedataset (str, optional): The dataset to query (e.g., 'blocks', 'transactions')blocks (str, optional): Block range specificationstart_block (int, optional): Start block numberend_block (int, optional): End block numberuse_latest (bool, optional): If True, query the latest blockblocks_from_latest (int, optional): Number of blocks before the latest to includecontract (str, optional): Contract address to filter byforce_refresh (bool, optional): Force download of new data even if it existsinclude_schema (bool, optional): Include schema information in the resultExample:
# Automatically downloads blocks data if needed, then runs the SQL query
client.query_blockchain_sql(
sql_query="SELECT block_number, gas_used, timestamp FROM blocks ORDER BY gas_used DESC LIMIT 10",
dataset="blocks",
blocks_from_latest=100
)
list_available_sql_tables()List all available tables that can be queried with SQL.
Example:
client.list_available_sql_tables()
get_sql_table_schema()Get the schema for a specific parquet file.
Parameters:
file_path (str): Path to the parquet fileExample:
client.get_sql_table_schema("/path/to/blocks.parquet")
get_sql_examples()Get example SQL queries for different blockchain datasets.
Example:
client.get_sql_examples()
When starting the Cryo MCP server, you can use these command-line options:
--rpc-url URL: Ethereum RPC URL (overrides ETH_RPC_URL environment variable)--data-dir PATH: Directory to store downloaded data (overrides CRYO_DATA_DIR environment variable, defaults to ~/.cryo-mcp/data/)ETH_RPC_URL: Default Ethereum RPC URL to use when not specified via command lineCRYO_DATA_DIR: Default directory to store downloaded data when not specified via command lineCryo MCP allows you to run powerful SQL queries against blockchain data, combining the flexibility of SQL with Cryo's data extraction capabilities:
You can split data extraction and querying into two separate steps:
# Step 1: Download data and get file paths
download_result = client.query_dataset(
dataset="transactions",
blocks_from_latest=1000,
output_format="parquet"
)
# Step 2: Use the file paths to run SQL queries
file_paths = download_result.get("files", [])
client.query_sql(
query=f"""
SELECT
to_address as contract_address,
COUNT(*) as tx_count,
SUM(gas_used) as total_gas,
AVG(gas_used) as avg_gas
FROM read_parquet('{file_paths[0]}')
WHERE to_address IS NOT NULL
GROUP BY to_address
ORDER BY total_gas DESC
LIMIT 20
""",
files=file_paths
)
For convenience, you can also use the combined function that handles both steps:
# Get top gas-consuming contracts
client.query_blockchain_sql(
sql_query="""
SELECT
to_address as contract_address,
COUNT(*) as tx_count,
SUM(gas_used) as total_gas,
AVG(gas_used) as avg_gas
FROM read_parquet('/path/to/transactions.parquet')
WHERE to_address IS NOT NULL
GROUP BY to_address
ORDER BY total_gas DESC
LIMIT 20
""",
dataset="transactions",
blocks_from_latest=1000
)
# Find blocks with the most transactions
client.query_blockchain_sql(
sql_query="""
SELECT
block_number,
COUNT(*) as tx_count
FROM read_parquet('/path/to/transactions.parquet')
GROUP BY block_number
ORDER BY tx_count DESC
LIMIT 10
""",
dataset="transactions",
blocks="15M:16M"
)
# Analyze event logs by topic
client.query_blockchain_sql(
sql_query="""
SELECT
topic0,
COUNT(*) as event_count
FROM read_parquet('/path/to/logs.parquet')
GROUP BY topic0
ORDER BY event_count DESC
LIMIT 20
""",
dataset="logs",
blocks_from_latest=100
)
Note: For SQL queries, always use output_format="parquet" when downloading data to ensure optimal performance with DuckDB. When using query_blockchain_sql, you should refer to the file paths directly in your SQL using the read_parquet() function.
Cryo MCP supports the full range of Cryo's block specification syntax:
# Using block numbers
client.query_dataset('transactions', blocks='15000000:15001000')
# Using K/M notation
client.query_dataset('logs', blocks='15M:15.01M')
# Using offsets from latest
client.query_dataset('blocks', blocks_from_latest=100)
Filter logs and other data by contract address:
# Get all logs for USDC contract
client.query_dataset('logs',
blocks='16M:16.1M',
contract='0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48')
Include only the columns you need:
# Get just block numbers and timestamps
client.query_dataset('blocks',
blocks='16M:16.1M',
include_columns=['number', 'timestamp'])
cryo-mcp/
├── cryo_mcp/ # Main package directory
│ ├── __init__.py # Package initialization
│ ├── server.py # Main MCP server implementation
│ ├── sql.py # SQL query functionality
├── tests/ # Test directory
│ ├── test_*.py # Test files
├── pyproject.toml # Project configuration
├── README.md # Project documentation
uv run pytest
MIT
FAQs
MCP server for querying Ethereum blockchain data using cryo
We found that cryo-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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