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!

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

1. Clone or download this repository:

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

2. Install dependencies:

npm install

3. 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

4. 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:

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

For Skills:

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

For Shared Functionality:

1. Add logic to appropriate client in src/api/
2. Update both MCP and Skills to use it
3. 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.

Related Projects

• GladOSv2 - Trading bot using this MCP server
• Model Context Protocol - Official MCP documentation

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