Mcp Chainladder
by datalatticecommunityv1.2.2
An MCP server for actuarial chain-ladder reserving that lets Claude compute IBNR, project ultimates, run Mack stochastic error estimates, check model assumptions, and parse loss triangles from CSV files. It provides tools for cumulative/incremental triangle conversion and a sample triangle for testing.
From the registry: Actuarial chain-ladder reserving: IBNR, Mack stochastic stats, and assumption diagnostics.
Install & configure
Prerequisites
- requiredInstall Python 3.10+Python 3.10 or newer is required to run the server.
- requiredInstall uv or pipxThe README suggests either running the server on demand with uvx or installing it with pipx. For the config shown, uvx is the preferred portable path.
Install in your client
Paste into the AI you're chatting with — it will figure out the rest
Please install the `mcp-chainladder` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Install Python 3.10+** — Python 3.10 or newer is required to run the server.
- **Install uv or pipx** — The README suggests either running the server on demand with uvx or installing it with pipx. For the config shown, uvx is the preferred portable path.
Canonical MCP server config (stdio transport):
- command: `uvx`
- args: ["mcp-chainladder"]
Note: The README also offers `pipx install mcp-chainladder` as an alternative install method, but the documented MCP config uses `uvx mcp-chainladder`. No environment variables are required for the free-tier tools. An optional Pro feature can use a local license file at `~/.chainladder/license` or a path pointed to by `CHAINLADDER_LICENSE_FILE`, but this is not needed for the standard server setup.
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 also offers `pipx install mcp-chainladder` as an alternative install method, but the documented MCP config uses `uvx mcp-chainladder`. No environment variables are required for the free-tier tools. An optional Pro feature can use a local license file at `~/.chainladder/license` or a path pointed to by `CHAINLADDER_LICENSE_FILE`, but this is not needed for the standard server setup.
Tools (8)
compute_chain_ladder
Runs end-to-end chain ladder reserving on a triangle, returning factors, ultimates, IBNR, and totals.
project_triangle
Fills the lower-right of a triangle with chain-ladder projections and returns a rectangular 2-D list.
mack_stochastic
Computes Mack (1993) stochastic standard errors and coefficients of variation for reserve estimates.
mack_diagnostics
Runs diagnostic tests on a triangle, including residuals, outliers, calendar-year sign test, independence test, and inflation trend.
parse_csv_triangle
Reads a triangle from a CSV file on disk and returns the parsed triangle and dimensions.
to_incremental
Converts a cumulative triangle to incremental values.
to_cumulative
Converts an incremental triangle to cumulative values.
sample_triangle
Returns the textbook 10×10 cumulative paid triangle example.
Prerequisites
- • Python 3.10
Packages
mcp-chainladder
pypiv1.2.2transport: stdio
Details
- Language
- Python
- License
- NOASSERTION
- Released
- May 14, 2026
- Last commit
- May 15, 2026
- Registry name
- io.github.datalattice/mcp-chainladder
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