> ## Documentation Index
> Fetch the complete documentation index at: https://docs.quantspace.limex.pro/llms.txt
> Use this file to discover all available pages before exploring further.
# Dataloader Server
> Market-data retrieval, TA-Lib indicators, and dataframe exports.
## 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.
```json theme={null}
{
"mcpServers": {
"dataloader": {
"url": "https://market-data-loader-production.up.railway.app/mcp/dataloader"
}
}
}
```
```json theme={null}
{
"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
```json theme={null}
{
"request": {
"tickers": [
"AAPL",
"MSFT"
],
"timeframe": "1d",
"start_date": "2024-01-01",
"end_date": "2024-03-31",
"source": "yahoo"
}
}
```
### Compute indicators
```json theme={null}
{
"request": {
"df": "$last",
"indicators": [
"SMA",
"RSI"
],
"params": {
"SMA": {
"timeperiod": 20
},
"RSI": {
"timeperiod": 14
}
}
}
}
```
### Save a dataframe
```json theme={null}
{
"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.
Configure this endpoint in Cursor, Claude Desktop, or a generic MCP client.
Use health, result, artifact, environment, and table helper tools.
## Other Servers
Official and replicated Fama-French factors plus loadings and alpha estimation.
Stock-Watson, complete-panel, and dynamic statistical factor extraction.
JumpModel and SparseJumpModel regime fitting, online prediction, and backtesting.
Wavelet-based mean reversion analysis for financial time series.
ExtremeHurst signal generation from OHLCV data.
Fundamental stock screening based on earnings yield and balance-sheet quality.
Volatility targeting, EWMA volatility, Monte Carlo bands, and risk diagnostics.