Tsbootstrap

Name: Tsbootstrap
Author: astrogilda

by astrogildacommunityv0.6.1

tsbootstrap generates bootstrap resamples from time-series data and provides confidence intervals, forecast intervals, and related uncertainty-quantification utilities. It also ships a read-only MCP server that can diagnose a short series and compute a bootstrap confidence interval via two tools over stdio.

From the registry: Read-only time-series bootstrap server: diagnose a series and compute confidence intervals.

GitHub Repository

Install & configure

Prerequisites

required
Install Python 3.10 or higher
The MCP server requires Python 3.10+.
required
Install uv
The README recommends running the MCP server with uvx and says it can be run with no install step using uvx.

Install in your client

Paste into the AI you're chatting with — it will figure out the rest

Please install the `tsbootstrap` MCP server into my current AI client (that's you).

Required prerequisites (do these first if not already done):
- **Install Python 3.10 or higher** — The MCP server requires Python 3.10+.
- **Install uv** — The README recommends running the MCP server with uvx and says it can be run with no install step using uvx.

Canonical MCP server config (stdio transport):
- command: `uvx`
- args: ["--from","tsbootstrap[mcp]","tsbootstrap-mcp"]

Note: The README describes a read-only MCP server exposed over stdio. No environment variables are documented. The README says the server can be run with no install step using uvx.

Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.

Note: The README describes a read-only MCP server exposed over stdio. No environment variables are documented. The README says the server can be run with no install step using uvx.

Tools (2)

diagnose_series

Runs serial-dependence and stationarity diagnostics, recommends a Politis-White block length, and lists supported bootstrap methods for the input series.

bootstrap_confidence_interval

Computes a percentile confidence interval for the mean, median, standard deviation, or variance using an i.i.d. or block bootstrap.