MCP 服务器

Databento MCP

A Model Context Protocol server that provides access to Databento's historical and real-time market data, including trades, OHLCV bars, and order book depth. It enables AI assistants to perform financial data analysis, manage batch jobs, and convert market data between DBN and Parquet formats.

README

<h1 align="center">Databento™ MCP</h1>

<p align="center"> <strong>Model Context Protocol server for Databento™ market data</strong> </p>

Installation

pip install databento-mcp

Quick Start

Get your API key from Databento
Configure your MCP client (see setup guides below)
Start querying market data through your AI assistant

Setup Guides

Claude Desktop

Add to your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "databento": {
      "command": "databento-mcp",
      "env": {
        "DATABENTO_API_KEY": "your-api-key"
      }
    }
  }
}

GitHub Copilot CLI

Add the server to your Copilot CLI configuration:

gh copilot config set mcp-servers '{
  "databento": {
    "command": "databento-mcp",
    "env": {
      "DATABENTO_API_KEY": "your-api-key"
    }
  }
}'

Or add to your ~/.config/gh-copilot/config.yml:

mcp-servers:
  databento:
    command: databento-mcp
    env:
      DATABENTO_API_KEY: your-api-key

See GitHub Copilot CLI MCP documentation for more details.

ChatGPT (via Developer Mode)

ChatGPT supports MCP servers through Developer Mode.

Enable Developer Mode in ChatGPT settings
Add an MCP server with the following configuration:

{
  "name": "databento",
  "command": "databento-mcp",
  "env": {
    "DATABENTO_API_KEY": "your-api-key"
  }
}

See OpenAI Developer Mode documentation for detailed setup instructions.

Features

Historical Data

Retrieve trades, OHLCV bars, market depth, and more
Support for all Databento schemas (trades, mbp-1, mbp-10, ohlcv-*, etc.)
Cost estimation before query execution
Smart data summaries with statistics

Live Data

Real-time market data streaming
Configurable stream duration
Multiple schema support

File Operations

Read/write DBN format files
Export to Apache Parquet
Convert between formats

Batch Processing

Submit large-scale batch jobs
Monitor job status
Download completed files

Reference Data

Symbol metadata and definitions
Symbology resolution
Dataset discovery
Publisher information

Quality & Performance

Smart caching with configurable TTL
Data quality analysis
Connection pooling
Comprehensive metrics

Available Tools

Tool	Description
`health_check`	Check API connectivity and server status
`get_historical_data`	Retrieve historical market data
`get_live_data`	Stream real-time market data
`get_cost`	Estimate query cost before execution
`get_symbol_metadata`	Get instrument definitions and mappings
`search_instruments`	Search for symbols with wildcards
`list_datasets`	List available Databento datasets
`list_schemas`	List available data schemas
`resolve_symbols`	Convert between symbology types
`submit_batch_job`	Submit batch data download
`list_batch_jobs`	List batch job status
`get_batch_job_files`	Get batch job download info
`cancel_batch_job`	Cancel pending batch job
`download_batch_files`	Download completed batch files
`read_dbn_file`	Parse and read DBN files
`get_dbn_metadata`	Get DBN file metadata
`write_dbn_file`	Write data to DBN format
`convert_dbn_to_parquet`	Convert DBN to Parquet
`export_to_parquet`	Query and export to Parquet
`read_parquet_file`	Read Parquet files
`get_session_info`	Get trading session info
`list_publishers`	List data publishers
`list_fields`	List schema fields
`get_dataset_range`	Get dataset date range
`list_unit_prices`	Get pricing information
`analyze_data_quality`	Analyze data quality issues
`quick_analysis`	Comprehensive symbol analysis
`get_account_status`	Server status and metrics
`get_metrics`	Performance metrics
`clear_cache`	Clear API response cache

Configuration

Environment Variable	Description	Default
`DATABENTO_API_KEY`	Databento API key (required)	-
`DATABENTO_DATA_DIR`	Restrict file operations to directory	Current directory
`DATABENTO_LOG_LEVEL`	Logging level (DEBUG, INFO, WARNING, ERROR)	INFO
`DATABENTO_METRICS_ENABLED`	Enable metrics collection	true

Common Datasets

Dataset	Description
`GLBX.MDP3`	CME Globex (ES, NQ, CL futures)
`XNAS.ITCH`	Nasdaq TotalView
`XNYS.PILLAR`	NYSE
`DBEQ.BASIC`	Consolidated US equities
`OPRA.PILLAR`	US options
`IFEU.IMPACT`	ICE Futures Europe

Common Schemas

Schema	Description
`trades`	Individual trades
`ohlcv-1m`	1-minute OHLCV bars
`ohlcv-1h`	1-hour OHLCV bars
`ohlcv-1d`	Daily OHLCV bars
`mbp-1`	Top of book
`mbp-10`	10-level order book
`tbbo`	Top bid/offer
`definition`	Instrument definitions

Development

# Clone repository
git clone https://github.com/deepentropy/databento-mcp.git
cd databento-mcp

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/
ruff check src/