Mcp Chainladder
Run chain-ladder IBNR reserving, Mack stochastic statistics, and assumption diagnostics from your actuarial agent workflow.
Overview
mcp-chainladder is a MCP server for the Build phase that performs actuarial chain-ladder IBNR reserving and Mack diagnostics through agent tools.
What is this MCP server?
- Chain-ladder reserving with IBNR outputs for loss development triangles
- Mack stochastic statistics for uncertainty around reserves
- Assumption diagnostics to challenge triangle inputs
- PyPI package mcp-chainladder runnable via uvx stdio (v1.2.2)
- Server version 1.2.2
- Capabilities: IBNR, Mack stochastic stats, assumption diagnostics
- Transport: stdio
What problem does it solve?
Small actuarial teams re-code chain-ladder and Mack routines for every agent experiment instead of calling a standard MCP tool surface.
Who is it for?
Solo actuaries, insurtech builders, and data consultants automating reserving workflows inside MCP-enabled dev environments.
Skip if: General SaaS founders without insurance triangles, or teams needing licensed signing workflows instead of analytical tooling.
What do I get? / Deliverables
After registration, your agent can produce IBNR reserves, stochastic stats, and assumption checks from triangle data in a repeatable integration.
- IBNR reserve estimates from chain-ladder runs
- Mack stochastic uncertainty outputs
- Assumption diagnostic feedback on triangle inputs
Recommended MCP Servers
Journey fit
Canonical shelf is Build integrations because the server plugs specialized reserving math into the agent during product or internal tooling construction. Integrations reflects wiring actuarial MCP tools into Claude or Cursor rather than broad go-to-market or ops monitoring tasks.
How it compares
Actuarial reserving MCP math layer—not a generic BI dashboard or payments integration.
Common Questions / FAQ
Who is mcp-chainladder for?
Actuarial professionals and insurtech solo builders who want chain-ladder reserving accessible from Claude Code, Cursor, or similar agents.
When should I use mcp-chainladder?
Use it while building or integrating internal reserving tools when you need IBNR, Mack stats, and diagnostics from the agent.
How do I add mcp-chainladder to my agent?
Run the mcp-chainladder PyPI package with uvx or pip in stdio mode, add the server to your MCP config, and supply triangle inputs through the documented tools.