How do I use DataBento MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@DataBento MCP Server get quote for ES futures" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

DataBento MCP Server

by Nice-Wolf-Studio

Overview Schema Related Servers Score Discussions

TypeScript

Remote

DataBento MCP Server & Skills

Professional market data access via DataBento API, available as both an MCP server and Claude Code skills.

What's New

Version 3.0 - Dual Deployment: MCP Server + Claude Code Skills

This project now supports two deployment modes:

MCP Server: For Claude Desktop and other MCP clients (18 tools)
Claude Code Skills: Native skills for Claude Code CLI (8 skill scripts)

Both modes share the same core functionality:

Complete Databento API coverage (Timeseries, Metadata, Batch, Symbology, Reference)
Full Historical API support with flexible schemas
Real-time futures quotes (ES, NQ)
Type-safe TypeScript implementation throughout

Choose the deployment that fits your workflow best!

Related MCP server: TradeStation MCP Server

Features

🎯 Real-time Futures Quotes - Current prices for ES and NQ contracts
📊 Historical Timeseries - Stream any market data schema across date ranges
📈 Batch Downloads - Submit and manage large historical data jobs
🔍 Symbol Resolution - Resolve symbols to instrument IDs across datasets
📚 Metadata Discovery - Explore datasets, schemas, fields, and pricing
🏢 Reference Data - Access security master, corporate actions, and adjustments
⏰ Session Detection - Automatic Asian/London/NY session identification
🚀 Rate Limiting - Built-in request throttling and caching (30s TTL)
🔒 Error Handling - Graceful failures with clear error messages

Installation

Prerequisites

Node.js v18+ or compatible runtime
DataBento API key (get one here)
For MCP: Claude Desktop or compatible MCP client
For Skills: Claude Code CLI

Setup

Clone or download this repository:

cd ~/Dev
git clone <your-repo-url> databento-mcp-server
cd databento-mcp-server

Install dependencies:

npm install

Create .env file with your DataBento API key:

cp .env.example .env
# Edit .env and add your API key

Your .env should contain:

DATABENTO_API_KEY=db-your-api-key-here
DATABENTO_DATASET=GLBX.MDP3

Choose your deployment mode below

Configuration

Option 1: MCP Server (for Claude Desktop)

Build the MCP server:

npm run build:mcp

Add to your Claude Desktop MCP configuration (~/.claude/mcp.json):

{
  "mcpServers": {
    "databento": {
      "command": "node",
      "args": ["/Users/yourusername/Dev/databento-mcp-server/dist/mcp/mcp/index.js"],
      "env": {
        "DATABENTO_API_KEY": "db-your-api-key-here"
      }
    }
  }
}

Or use npx directly (if published to npm):

{
  "mcpServers": {
    "databento": {
      "command": "npx",
      "args": ["-y", "databento-mcp-server"],
      "env": {
        "DATABENTO_API_KEY": "db-your-api-key-here"
      }
    }
  }
}

Option 2: Claude Code Skills

Build and install skills:

npm run install:skills

This will:

Compile the skills from TypeScript
Copy them to ~/.claude/skills/databento/
Make scripts executable

Set your API key environment variable:

export DATABENTO_API_KEY="db-your-api-key-here"
# Or add to your .bashrc/.zshrc for persistence

Verify installation:

node ~/.claude/skills/databento/scripts/get-quote.js ES

Environment Variables

Variable	Required	Default	Description
`DATABENTO_API_KEY`	✅	-	Your DataBento API key (starts with `db-`)
`DATABENTO_DATASET`	❌	`GLBX.MDP3`	CME dataset for futures data

Available Tools

The MCP server provides 18 tools organized into 6 categories:

Category	Tools	Description
Original	3 tools	ES/NQ futures quotes, session info, historical bars
Timeseries	1 tool	Historical market data streaming with flexible schemas
Symbology	1 tool	Symbol resolution and conversion
Metadata	6 tools	Dataset discovery, schema info, cost estimation
Batch	3 tools	Large-scale data download job management
Reference	3 tools	Security master, corporate actions, price adjustments

Original Tools (Futures & Session)

1. `get_futures_quote`

Get current price quote for ES or NQ futures.

Input:

{
  "symbol": "ES"
}

Output:

{
  "symbol": "ES",
  "price": 5845.25,
  "bid": 5845.00,
  "ask": 5845.50,
  "spread": 0.50,
  "timestamp": "2024-10-02T14:30:00.000Z",
  "dataAge": "15s ago",
  "source": "DataBento"
}

2. `get_session_info`

Get current trading session information.

Input:

{
  "timestamp": "2024-10-02T14:30:00Z"
}

Note: timestamp is optional, defaults to current time

Output:

{
  "currentSession": "NY",
  "sessionStart": "2024-10-02T14:00:00.000Z",
  "sessionEnd": "2024-10-02T22:00:00.000Z",
  "timestamp": "2024-10-02T14:30:00.000Z",
  "utcHour": 14
}

Sessions:

Asian: 00:00 - 07:00 UTC
London: 07:00 - 14:00 UTC
NY: 14:00 - 22:00 UTC

3. `get_historical_bars`

Get historical OHLCV bars for futures contracts.

Input:

{
  "symbol": "NQ",
  "timeframe": "H4",
  "count": 10
}

Output:

{
  "symbol": "NQ",
  "timeframe": "H4",
  "count": 10,
  "bars": [
    {
      "timestamp": "2024-10-02T00:00:00.000Z",
      "open": 20150.25,
      "high": 20175.50,
      "low": 20145.00,
      "close": 20160.75,
      "volume": 125000
    }
  ]
}

Supported Timeframes:

1h - Hourly bars
H4 - 4-hour bars (aggregated from 1h)
1d - Daily bars

Timeseries Tools

4. `timeseries_get_range`

Stream historical market data with flexible schemas and date ranges. Supports all Databento schemas.

Input:

{
  "dataset": "GLBX.MDP3",
  "symbols": "ES.c.0,NQ.c.0",
  "schema": "trades",
  "start": "2024-10-01",
  "end": "2024-10-02",
  "stype_in": "raw_symbol",
  "stype_out": "instrument_id",
  "limit": 1000
}

Supported Schemas:

mbp-1, mbp-10 - Market by price (1 or 10 levels)
mbo - Market by order
trades - Trade data
ohlcv-1s, ohlcv-1m, ohlcv-1h, ohlcv-1d, ohlcv-eod - OHLCV bars
statistics, definition, imbalance, status - Market metadata

Output:

{
  "dataset": "GLBX.MDP3",
  "schema": "trades",
  "symbols": ["ES.c.0"],
  "dateRange": {
    "start": "2024-10-01T00:00:00Z",
    "end": "2024-10-02T00:00:00Z"
  },
  "recordCount": 1000,
  "data": [
    {
      "ts_event": "2024-10-01T09:30:00.123456789Z",
      "price": 5845.25,
      "size": 10,
      "side": "B"
    }
  ]
}

Symbology Tools

5. `symbology_resolve`

Resolve symbols to instrument IDs or other symbol types across a date range.

Input:

{
  "dataset": "GLBX.MDP3",
  "symbols": ["ES", "NQ"],
  "stype_in": "continuous",
  "stype_out": "instrument_id",
  "start_date": "2024-10-01",
  "end_date": "2024-10-02"
}

Symbol Types:

raw_symbol - Native exchange symbol
instrument_id - Databento instrument ID
continuous - Continuous futures (c.0, c.1, etc.)
parent - Parent symbol
nasdaq, cms, bats, smart - Venue-specific symbology

Output:

{
  "dataset": "GLBX.MDP3",
  "stype_in": "continuous",
  "stype_out": "instrument_id",
  "date_range": {
    "start": "2024-10-01",
    "end": "2024-10-02"
  },
  "symbol_count": 2,
  "result": "partial",
  "mappings": [
    {
      "input_symbol": "ES.c.0",
      "output_symbol": "123456",
      "start_date": "2024-10-01",
      "end_date": "2024-10-02"
    }
  ]
}

Metadata Tools

6. `metadata_list_datasets`

List all available Databento datasets with optional date range filtering.

Input:

{
  "start_date": "2024-01-01",
  "end_date": "2024-12-31"
}

Output:

{
  "datasets": [
    {
      "dataset": "GLBX.MDP3",
      "description": "CME Globex MDP 3.0",
      "start_date": "2020-01-01",
      "end_date": null
    }
  ],
  "count": 1
}

7. `metadata_list_schemas`

List available data schemas for a specific dataset.

Input:

{
  "dataset": "GLBX.MDP3"
}

Output:

{
  "dataset": "GLBX.MDP3",
  "schemas": ["trades", "mbp-1", "mbp-10", "ohlcv-1h", "ohlcv-1d"],
  "count": 5
}

8. `metadata_list_publishers`

List publishers with their details, optionally filtered by dataset.

Input:

{
  "dataset": "GLBX.MDP3"
}

Output:

{
  "publishers": [
    {
      "publisher_id": 1,
      "dataset": "GLBX.MDP3",
      "venue": "CME",
      "description": "Chicago Mercantile Exchange"
    }
  ],
  "count": 1,
  "dataset_filter": "GLBX.MDP3"
}

9. `metadata_list_fields`

List fields available for a specific schema with their types and descriptions.

Input:

{
  "schema": "trades",
  "encoding": "json"
}

Output:

{
  "schema": "trades",
  "encoding": "json",
  "fields": [
    {
      "name": "ts_event",
      "type": "uint64",
      "description": "Event timestamp in nanoseconds"
    },
    {
      "name": "price",
      "type": "int64",
      "description": "Price in fixed-point notation"
    }
  ],
  "count": 2
}

10. `metadata_get_cost`

Calculate the cost in USD for a historical data query before downloading.

Input:

{
  "dataset": "GLBX.MDP3",
  "symbols": "ES.c.0",
  "schema": "trades",
  "start": "2024-10-01",
  "end": "2024-10-02",
  "stype_in": "raw_symbol"
}

Output:

{
  "dataset": "GLBX.MDP3",
  "symbols": ["ES.c.0"],
  "schema": "trades",
  "cost_usd": 15.50,
  "record_count_estimate": 1500000,
  "size_bytes_estimate": 45000000
}

11. `metadata_get_dataset_range`

Get the available date range for a dataset.

Input:

{
  "dataset": "GLBX.MDP3"
}

Output:

{
  "dataset": "GLBX.MDP3",
  "start_date": "2020-01-01",
  "end_date": null,
  "description": "Data available from 2020-01-01 to present"
}

Batch Tools

12. `batch_submit_job`

Submit a batch data download job for large historical datasets. Returns job ID and status.

Input:

{
  "dataset": "GLBX.MDP3",
  "symbols": ["ES.c.0", "NQ.c.0"],
  "schema": "trades",
  "start": "2024-10-01",
  "end": "2024-10-02",
  "encoding": "csv",
  "compression": "zstd",
  "stype_in": "raw_symbol",
  "split_duration": "day"
}

Output:

{
  "status": "submitted",
  "job_id": "abc123def456",
  "state": "received",
  "dataset": "GLBX.MDP3",
  "schema": "trades",
  "symbols_count": 2,
  "cost_usd": 25.00,
  "date_range": {
    "start": "2024-10-01",
    "end": "2024-10-02"
  },
  "encoding": "csv",
  "compression": "zstd",
  "ts_received": "2024-10-03T10:00:00Z",
  "message": "Job submitted successfully. Use batch_list_jobs or batch_download to check status and download files when ready."
}

13. `batch_list_jobs`

List all batch jobs with their current status. Optionally filter by job states or time range.

Input:

{
  "states": ["done", "processing"],
  "since": "2024-10-01T00:00:00Z"
}

Output:

{
  "total_jobs": 5,
  "jobs_by_state": {
    "done": 3,
    "processing": 2
  },
  "jobs": [
    {
      "id": "abc123def456",
      "state": "done",
      "dataset": "GLBX.MDP3",
      "schema": "trades",
      "symbols_count": 2,
      "cost_usd": 25.00,
      "date_range": {
        "start": "2024-10-01",
        "end": "2024-10-02"
      },
      "record_count": 1500000,
      "file_count": 2,
      "total_size_bytes": 45000000,
      "ts_received": "2024-10-03T10:00:00Z",
      "ts_process_done": "2024-10-03T10:15:00Z",
      "ts_expiration": "2024-10-10T10:00:00Z"
    }
  ]
}

14. `batch_download`

Get download information for a completed batch job. Returns download URLs and metadata.

Input:

{
  "job_id": "abc123def456"
}

Output:

{
  "job_id": "abc123def456",
  "state": "done",
  "files": [
    {
      "filename": "20241001.csv.zst",
      "size_bytes": 22500000,
      "hash": "sha256:abc123...",
      "download_url": "https://download.databento.com/..."
    }
  ],
  "total_size_bytes": 45000000,
  "expiration": "2024-10-10T10:00:00Z"
}

Reference Tools

15. `reference_search_securities`

Search security master database for instrument metadata.

Input:

{
  "dataset": "GLBX.MDP3",
  "symbols": "ES.c.0,NQ.c.0",
  "start_date": "2024-10-01",
  "end_date": "2024-10-02",
  "limit": 100
}

Output:

{
  "dataset": "GLBX.MDP3",
  "symbols": "ES.c.0,NQ.c.0",
  "date_range": {
    "start": "2024-10-01",
    "end": "2024-10-02"
  },
  "record_count": 2,
  "securities": [
    {
      "instrument_id": "123456",
      "raw_symbol": "ESZ4",
      "description": "E-mini S&P 500 Dec 2024",
      "asset_class": "futures",
      "exchange": "CME",
      "currency": "USD",
      "first_date": "2023-09-18",
      "last_date": "2024-12-20",
      "min_price_increment": 0.25,
      "display_factor": 1.0
    }
  ]
}

16. `reference_get_corporate_actions`

Get corporate actions (dividends, splits, etc.) for symbols.

Input:

{
  "dataset": "XNAS.ITCH",
  "symbols": "AAPL,MSFT",
  "start_date": "2024-01-01",
  "end_date": "2024-12-31",
  "action_types": ["dividend", "split"]
}

Output:

{
  "dataset": "XNAS.ITCH",
  "symbols": "AAPL,MSFT",
  "date_range": {
    "start": "2024-01-01",
    "end": "2024-12-31"
  },
  "record_count": 5,
  "action_types_filter": ["dividend", "split"],
  "corporate_actions": [
    {
      "instrument_id": "789012",
      "raw_symbol": "AAPL",
      "action_type": "dividend",
      "ex_date": "2024-05-10",
      "record_date": "2024-05-13",
      "payment_date": "2024-05-16",
      "amount": 0.25,
      "currency": "USD"
    }
  ]
}

17. `reference_get_adjustments`

Get price adjustment factors for backadjusted prices.

Input:

{
  "dataset": "XNAS.ITCH",
  "symbols": "AAPL",
  "start_date": "2024-01-01",
  "end_date": "2024-12-31"
}

Output:

{
  "dataset": "XNAS.ITCH",
  "symbols": "AAPL",
  "date_range": {
    "start": "2024-01-01",
    "end": "2024-12-31"
  },
  "record_count": 2,
  "adjustments": [
    {
      "instrument_id": "789012",
      "raw_symbol": "AAPL",
      "adjustment_date": "2024-05-10",
      "adjustment_type": "dividend",
      "price_factor": 0.998654,
      "volume_factor": 1.0
    }
  ]
}

Usage Examples

With Claude Desktop

Once configured, you can ask Claude:

Original Futures Tools:

"What's the current ES price?"

Claude will use the get_futures_quote tool to fetch real-time data.

"Get the last 10 H4 bars for NQ"

Claude will use the get_historical_bars tool.

"What session are we in right now?"

Claude will use the get_session_info tool.

New Databento API Tools:

"List all available Databento datasets"

Claude will use metadata_list_datasets to show all available datasets.

"Get trade data for ES on October 1st"

Claude will use timeseries_get_range to fetch historical trade data.

"Resolve the symbol ES.c.0 to instrument ID"

Claude will use symbology_resolve to convert symbol types.

"How much would it cost to download all trades for AAPL in September?"

Claude will use metadata_get_cost to calculate the query cost.

"Submit a batch job for NQ trade data from last week"

Claude will use batch_submit_job to create a batch download job.

"Get security details for ESZ4"

Claude will use reference_search_securities to fetch instrument metadata.

"Get dividend history for AAPL in 2024"

Claude will use reference_get_corporate_actions to fetch corporate actions.

Development Mode

Run the server in development mode with auto-reload:

npm run dev

Production Mode

Build and run:

npm run build
npm start

Technical Details

Data Provider

Source: DataBento CME futures data
Symbols: ES.c.0 (S&P 500), NQ.c.0 (Nasdaq-100)
Dataset: GLBX.MDP3 (CME Globex MDP 3.0)
Precision: Nanosecond timestamps, 1e9 price units

Caching Strategy

Quote Cache: 30-second TTL (reduces API calls)
Weekend Handling: 7-day lookback for off-hours data
Rate Limiting: Built-in request throttling

Error Handling

All tools return structured errors:

{
  "error": "No quote data available for ES"
}

Common errors:

Missing API key
Invalid symbol (only ES/NQ supported)
No data available (weekends, holidays)
API rate limit exceeded

Claude Code Skills Usage

Once installed, the skills can be invoked naturally in Claude Code:

Get real-time quote:

> Get the current ES futures quote

Historical data:

> Fetch 50 daily bars for NQ

Symbol resolution:

> Resolve ESM4 symbol to instrument ID in GLBX.MDP3

Metadata queries:

> List all available schemas for GLBX.MDP3 dataset

Batch operations:

> List my databento batch jobs

The skills are automatically detected based on context and keywords.

Project Structure

databento-mcp-server/
├── src/                      # Shared code (used by both MCP & Skills)
│   ├── databento-client.ts   # Futures client (quotes, bars, sessions)
│   ├── http/
│   │   └── databento-http.ts # Base HTTP client with auth, retry, caching
│   ├── api/                  # API clients
│   │   ├── metadata-client.ts
│   │   ├── timeseries-client.ts
│   │   ├── batch-client.ts
│   │   ├── symbology-client.ts
│   │   └── reference-client.ts
│   └── types/                # TypeScript type definitions
│       ├── metadata.ts
│       ├── timeseries.ts
│       ├── batch.ts
│       ├── symbology.ts
│       └── reference.ts
├── mcp/                      # MCP Server specific code
│   └── index.ts              # MCP server entry point & 18 tool definitions
├── skills/                   # Claude Code Skills
│   ├── databento/
│   │   ├── skill.md          # Skill documentation
│   │   ├── scripts/          # 8 executable skill scripts
│   │   │   ├── get-quote.ts
│   │   │   ├── get-historical.ts
│   │   │   ├── get-session.ts
│   │   │   ├── resolve-symbols.ts
│   │   │   ├── timeseries.ts
│   │   │   ├── metadata.ts
│   │   │   ├── batch.ts
│   │   │   └── reference.ts
│   │   └── data/
│   └── manifest.json         # Skills manifest
├── scripts/
│   └── install-skills.sh     # Skill installation script
├── dist/                     # Compiled JavaScript (build output)
│   ├── mcp/                  # MCP server build
│   ├── skills/               # Skills build
│   └── src/                  # Shared code build
├── docs/
│   ├── adrs/                 # Architecture Decision Records
│   └── journals/             # Implementation journals
├── tsconfig.json             # Base TypeScript config
├── tsconfig.mcp.json         # MCP build config
├── tsconfig.skills.json      # Skills build config
├── package.json
├── .env.example
└── README.md

Development

Building

Build everything:

npm run build

Build MCP server only:

npm run build:mcp

Build skills only:

npm run build:skills

Adding New Functionality

For MCP Server:

Add tool definition to ListToolsRequestSchema handler in mcp/index.ts
Implement handler in CallToolRequestSchema switch statement
Add client method to appropriate API client in src/api/
Rebuild: npm run build:mcp

For Skills:

Create new script in skills/databento/scripts/
Import and use shared clients from src/
Update skills/manifest.json with new script
Rebuild and install: npm run install:skills

For Shared Functionality:

Add logic to appropriate client in src/api/
Update both MCP and Skills to use it
Rebuild both: npm run build

Testing Locally

# Set API key
export DATABENTO_API_KEY=db-your-key

# Run dev server
npm run dev

Limitations

Original Tools: get_futures_quote and get_historical_bars only support ES and NQ futures
New Tools: Support all Databento datasets and symbols (GLBX.MDP3, XNAS.ITCH, DBEQ.BASIC, etc.)
Data Delay: Historical API (not tick-by-tick real-time streaming)
Weekend Data: May show stale data on weekends/holidays
Rate Limits: Respects DataBento API limits (60 req/min)
Batch Downloads: Download URLs are returned but file content is not streamed through MCP
API Key Permissions: Access to datasets requires appropriate Databento subscriptions

Troubleshooting

"DATABENTO_API_KEY is required"

Ensure your .env file contains a valid API key starting with db-.

"No quote data available"

Check if markets are open (futures trade 23h/day on weekdays)
Verify your DataBento account has CME futures access
Check API key permissions

"HTTP 401" errors

Your API key is invalid or expired. Get a new one from databento.com.

License

MIT

Contributing

Contributions welcome! Please open issues or PRs on GitHub.

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DataBento MCP Server & Skills

What's New

Features

Installation

Prerequisites

Setup

Configuration

Option 1: MCP Server (for Claude Desktop)

Option 2: Claude Code Skills

Environment Variables

Available Tools

Original Tools (Futures & Session)

1. get_futures_quote

2. get_session_info

3. get_historical_bars

Timeseries Tools

4. timeseries_get_range

Symbology Tools

5. symbology_resolve

Metadata Tools

6. metadata_list_datasets

7. metadata_list_schemas

8. metadata_list_publishers

9. metadata_list_fields

10. metadata_get_cost

11. metadata_get_dataset_range

Batch Tools

12. batch_submit_job

13. batch_list_jobs

14. batch_download

Reference Tools

15. reference_search_securities

16. reference_get_corporate_actions

17. reference_get_adjustments

Usage Examples

With Claude Desktop

Development Mode

Production Mode

Technical Details

Data Provider

Caching Strategy

Error Handling

Claude Code Skills Usage

Project Structure

Development

Building

Adding New Functionality

Testing Locally

Limitations

Troubleshooting

"DATABENTO_API_KEY is required"

"No quote data available"

"HTTP 401" errors

License

Contributing

Related Projects

Maintenance

Resources

Looking for Admin?

Tools

Latest Blog Posts

MCP directory API

1. `get_futures_quote`

2. `get_session_info`

3. `get_historical_bars`

4. `timeseries_get_range`

5. `symbology_resolve`

6. `metadata_list_datasets`

7. `metadata_list_schemas`

8. `metadata_list_publishers`

9. `metadata_list_fields`

10. `metadata_get_cost`

11. `metadata_get_dataset_range`

12. `batch_submit_job`

13. `batch_list_jobs`

14. `batch_download`

15. `reference_search_securities`

16. `reference_get_corporate_actions`

17. `reference_get_adjustments`