Dataloader Server

Overview

The Dataloader Server loads OHLCV data, computes TA-Lib indicators, and persists derived tables. Use it as the market-data and indicator-calculation entry point.

Connection

Add this server to your MCP client configuration.

Cursor
Bearer auth

{
  "mcpServers": {
    "dataloader": {
      "url": "https://market-data-loader-production.up.railway.app/mcp/dataloader"
    }
  }
}

{
  "mcpServers": {
    "dataloader": {
      "url": "https://market-data-loader-production.up.railway.app/mcp/dataloader",
      "headers": {
        "Authorization": "Bearer ${env:DATALOADER_MCP_TOKEN}"
      }
    }
  }
}

Restart the client after changing MCP configuration. The server tools appear automatically after the connection is established.

Transport

Property	Value
Protocol	MCP over Streamable HTTP
MCP URL	`https://market-data-loader-production.up.railway.app/mcp/dataloader`
Health URL	`https://market-data-loader-production.up.railway.app/health`
MCP path	`/mcp/dataloader`
Request envelope	`{"request": {...}}`
Auth	Optional bearer token when enabled for the endpoint

Best-Fit Workflows

Load OHLCV data for one or more tickers.
Compute TA-Lib indicators on tabular OHLCV payloads.
Inspect supported indicators.
Export CSV, Parquet, or pandas pickle outputs.

Recommended Tools

fetch-ohlcvs
compute-indicators
list-supported-indicators
save-dataframe

Tools

Tool	Description	Returns
`list-supported-indicators`	Returns a sorted list of indicator names supported by TA-Lib, enabling developers to see which technical analysis functions are available. Params: none	`list[str]`
`compute-indicators`	Calculates TA-Lib technical indicators from OHLCV data, supporting single or multi-ticker inputs with optional parameter overrides. Params: df: OHLCV DataFrame; indicators: list of TA-Lib indicator names; params: dict of indicator-specific parameters (optional); ticker: prefix to filter multi-ticker data (optional)	`dict[str, Any]`
`fetch-ohlcvs`	Loads OHLCV data for one or multiple tickers from Yahoo or other sources, returning a combined DataFrame and metadata. This is the retrieval step, not the file-export step. If the user wants a local CSV/Parquet file, follow with save-dataframe instead of manually writing rows. Params: Inputs: tickers (list of symbols), timeframe (e.g., ‘1d’, ‘1h’, ‘5m’), start_date, end_date, source (optional), interval (legacy alias). Returns dict with ohlcv_df, ticker_details, row_count, columns.	`dict[str, pd.DataFrame \| list[dict[str, str \| list[str]]] \| int \| list[str]]`
`save-dataframe`	Persist a dataframe to disk and return the absolute file path. This is the primary tool for user requests like ‘download this data’, ‘save to Downloads’, or ‘export to csv/parquet’. Prefer it over fetching rows and manually writing a file. `ticker` and `dataset_name` are required identifiers and must be provided explicitly; they are not inferred from the dataframe payload. Supports CSV, Parquet, and pandas pickle formats, with automatic filename generation based on ticker, timeframe, and date range. Params: Inputs: dataframe (Any), ticker (str), dataset_name (str), timeframe (str\|None), start_date (str\|None), end_date (str\|None), file_format (str, default=‘csv’), data_kind (str, default=‘auto’), interval (str\|None). Example: timeframe=‘1h’, file_format=‘parquet’, data_kind=‘ohlcv’.	`str`

Examples

Load OHLCV data

{
  "request": {
    "tickers": [
      "AAPL",
      "MSFT"
    ],
    "timeframe": "1d",
    "start_date": "2024-01-01",
    "end_date": "2024-03-31",
    "source": "yahoo"
  }
}

Compute indicators

{
  "request": {
    "df": "$last",
    "indicators": [
      "SMA",
      "RSI"
    ],
    "params": {
      "SMA": {
        "timeperiod": 20
      },
      "RSI": {
        "timeperiod": 14
      }
    }
  }
}

Save a dataframe

{
  "request": {
    "dataframe": "$last",
    "ticker": "MSFT",
    "dataset_name": "ta_features",
    "timeframe": "1d",
    "file_format": "csv"
  }
}

Notes

If no data source is specified and the premium provider key is unavailable, the effective source falls back to Yahoo.
Large tabular outputs can be returned as result handles; use shared result/artifact tools when that happens.

Client setup

Configure this endpoint in Cursor, Claude Desktop, or a generic MCP client.

Shared tools

Use health, result, artifact, environment, and table helper tools.

Other Servers

Fama-French Replicate

Official and replicated Fama-French factors plus loadings and alpha estimation.

Statistical Factor Models

Stock-Watson, complete-panel, and dynamic statistical factor extraction.

Jump Models

JumpModel and SparseJumpModel regime fitting, online prediction, and backtesting.

Wavelet Mean Reversion

Wavelet-based mean reversion analysis for financial time series.

Parallax ExtremeHurst

ExtremeHurst signal generation from OHLCV data.

EP Ratio Screener

Fundamental stock screening based on earnings yield and balance-sheet quality.

Volatility Scaling Lab

Volatility targeting, EWMA volatility, Monte Carlo bands, and risk diagnostics.

Overview

MCP Setup

Fama-French Replicate Server

Statistical Factor Models Server

Jump Models Server

Wavelet Mean Reversion Server

Parallax ExtremeHurst Server

EP Ratio Screener Server

Volatility Scaling Lab Server

Dataloader Server

Overview

Connection

Transport

Best-Fit Workflows

Recommended Tools

Tools

Examples

Load OHLCV data

Compute indicators

Save a dataframe

Notes

Client setup

Shared tools

Other Servers

Fama-French Replicate

Statistical Factor Models

Jump Models

Wavelet Mean Reversion

Parallax ExtremeHurst

EP Ratio Screener

Volatility Scaling Lab

​Overview

​Connection

​Transport

​Best-Fit Workflows

​Recommended Tools

​Tools

​Examples

​Load OHLCV data

​Compute indicators

​Save a dataframe

​Notes

Client setup

Shared tools

​Other Servers

Fama-French Replicate

Statistical Factor Models

Jump Models

Wavelet Mean Reversion

Parallax ExtremeHurst

EP Ratio Screener

Volatility Scaling Lab

Overview

Connection

Transport

Best-Fit Workflows

Recommended Tools

Tools

Examples

Load OHLCV data

Compute indicators

Save a dataframe

Notes

Other Servers