Databento MCP

Check the health and connectivity of the Databento API. Use this to diagnose connection issues or verify the server is working properly.

Retrieve historical market data for symbols from Databento.

Examples:

• Get ES futures trades: dataset="GLBX.MDP3", symbols="ES.FUT", start="2024-01-15", end="2024-01-15", schema="trades"

• Get AAPL OHLCV bars: dataset="XNAS.ITCH", symbols="AAPL", start="2024-01-01", end="2024-01-31", schema="ohlcv-1m"

• Get BTC order book: dataset="GLBX.MDP3", symbols="BTC.FUT", schema="mbp-10"

Tips:

• Use explain=true to preview query without executing

• Use force_refresh=true to bypass cache

• Start with small date ranges and use limit parameter

Get metadata for symbols including symbology mappings and instrument definitions

Search for instruments in a dataset

List all available datasets from Databento

Clear the API response cache

Estimate the cost of a historical data query before executing it

Subscribe to real-time market data for a limited duration

Resolve symbols between different symbology types

Submit a batch data download job for large historical datasets

List all batch jobs with their current status

Get download information for a completed batch job

Identify the current trading session based on time

List data publishers with their details

List fields available for a specific schema

Get the available date range for a dataset

Read and parse a DBN file, returning the records as structured data

Get only the metadata from a DBN file without reading all records

Write historical data query results directly to a DBN file

Convert a DBN file to Parquet format

Query historical data and export directly to Parquet format

Read a Parquet file and return the data

Get server performance metrics and usage statistics.

Returns:

• Server uptime

• API call counts

• Cache hit/miss rates

• Per-tool latency and success rates

Example: get_metrics(reset=false) to view current stats

Get comprehensive server status and account information.

Returns:

• Server uptime and health

• Cache statistics (hit rate, entries)

• API usage metrics

• Tool usage breakdown

• Memory and performance stats

Example: get_account_status() for a full server overview

One-call comprehensive analysis of a symbol.

Combines: metadata + cost estimate + sample data + trading session info + data quality check.

Example:

• quick_analysis(dataset="GLBX.MDP3", symbol="ES.FUT", date="2024-01-15")

• quick_analysis(dataset="XNAS.ITCH", symbol="AAPL", date="2024-01-15", schema="ohlcv-1m")

Returns:

• Symbol metadata and instrument info

• Cost estimate for full-day data

• Sample of recent trades/bars

• Current trading session context

• Data quality assessment

Analyze data quality and detect issues in market data.

Detects:

• Time gaps in data

• Price outliers (>3 standard deviations)

• Null values and missing data

• Duplicate records

Returns:

• Quality score (0-100)

• List of issues and warnings

• Detailed breakdown of problems

Example:

• First retrieve data with get_historical_data

• Then analyze_data_quality(dataset="GLBX.MDP3", symbols="ES.FUT", start="2024-01-15", end="2024-01-15")

List all available data schemas from Databento.

Returns:

• List of all available schemas with descriptions

• Includes trades, OHLCV bars, order book, and reference data schemas

Example: list_schemas()

Get current pricing information per dataset/schema combination.

Parameters:

• dataset (optional) - Filter by dataset name (e.g., "GLBX.MDP3")

Returns:

• List of unit prices showing cost per GB or per record

• Helps understand pricing before querying

Example: list_unit_prices(dataset="GLBX.MDP3")

Cancel a pending or processing batch job.

Parameters:

• job_id (required) - The batch job ID to cancel

Returns:

• Success/failure status

• Final job state

• Message explaining the result

Example: cancel_batch_job(job_id="JOB-12345")

Download completed batch job files to a local directory.

Parameters:

• job_id (required) - The batch job ID

• output_dir (required) - Directory to save downloaded files

• overwrite (optional, default: false) - Whether to overwrite existing files

Returns:

• List of downloaded files with paths and sizes

• Total download size

• Success/failure status for each file

Example: download_batch_files(job_id="JOB-12345", output_dir="/data/downloads")