mcp-chainladder

mcp-chainladder

Actuarial chain-ladder reserving for Claude. A Model Context Protocol server that hands Claude the tools to compute IBNR, project ultimates, run Mack (1993) stochastic error estimates, check Mack (1994) model assumptions, and parse loss triangles from CSV — all from a natural- language conversation.

Calculation only — not actuarial advice. This is a mechanical chain-ladder calculator. Selection of data, factors, tails, and exclusions, and the interpretation of stochastic error measures, require professional actuarial judgement.

What it does

Eight tools, exposed over MCP. Claude picks the right one when you ask a question; you don't have to call them by name.

Tool	When Claude reaches for it
compute_chain_ladder	"What's the IBNR on this triangle?" — the workhorse
project_triangle	"What does the full projected triangle look like?"
mack_stochastic	"What's the uncertainty on the total reserve?"
mack_diagnostics	"Are there outliers or trend issues?"
parse_csv_triangle	"Run the chain ladder on this CSV file"
to_incremental	"Show me the incremental development pattern"
to_cumulative	"Cumulate these incremental values"
sample_triangle	"Show me a working example" — quick demo

All numerical conventions match the Mack 1993 and Mack 1994 papers; results are stable to ~1e-12 against hand calculation on the textbook 10×10 triangle.

Install

pipx install mcp-chainladder
# or, to run on demand without installing:
uvx mcp-chainladder

Python 3.10 or newer; no other runtime dependencies beyond the official MCP SDK.

Hook into Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "chainladder": {
      "command": "uvx",
      "args": ["mcp-chainladder"]
    }
  }
}

Quit and reopen Claude Desktop. The triangle tools appear under the 🔌 icon in any conversation.

For Cursor / Cline / Continue / Zed — same mcpServers block, each app has its own config file location (their docs walk you through it).

Examples

Run the chain ladder on a CSV

Me: I have a paid-loss triangle at ~/Desktop/motor_2026Q1.csv.
    Run the chain ladder and tell me the IBNR.

Claude: [calls parse_csv_triangle, then compute_chain_ladder]
        Loaded a 10×10 cumulative triangle. Using volume-weighted
        factors:
          • Total paid to date: 49,458,000 USD
          • Total ultimate:     65,883,400 USD
          • Total IBNR:         16,425,400 USD
        Selected factors trend from 1.879 at the front of the
        triangle down to 1.002 at the tail.

Check whether you should worry about assumptions

Me: Same triangle — any reason not to trust the projection?

Claude: [calls mack_diagnostics]
        Three flags worth a look:
        • Inflation trend: slope 0.053, p ≈ 1e-12 — STRONG evidence
          link ratios are drifting upward across accident years.
          This violates Mack's homogeneity assumption; consider
          weighting recent years more.
        • Calendar-year: Z = 0.0, p = 1.0 — no diagonal effect.
        • Independence: Z = -0.35, p = 0.73 — no obvious dependence
          across development columns.
        • Outliers: 0 cells exceed |2σ|.

        The inflation finding is the one to act on.

Exclude an outlier and re-run

Me: Drop the link ratio at AY 3, dev period 2→3. What changes?

Claude: [calls compute_chain_ladder with excluded=[[2, 1]]]
        Excluding (AY 3, 2→3):
          • Volume factor at 2→3 moves from 1.331 to 1.328
          • Total IBNR moves from 16,425 to 16,389 (−36)
          • Mack SE total moves from ±354.6 to ±348.2
        Net: small enough to be a "robustness check passes" rather
        than a finding.

Tool reference

Each tool returns a JSON object (or a 2-D list, in the case of project_triangle/to_incremental/to_cumulative). Claude reads the descriptions and types directly from the server — you don't need to memorise the shapes — but here's the cheat sheet.

compute_chain_ladder(triangle, selected_factors?, tail?, excluded?)

End-to-end chain ladder.

Field returned	Meaning
volume_factors[j]	All-year volume-weighted age-to-age factor for transition j→j+1
simple_factors[j]	Unweighted average of individual link ratios
selected_factors[j]	The factor set actually used to project (defaults to volume)
individual_factors[i][j]	Per-row link ratio C[i,j+1] / C[i,j]; null where the pair is unobserved
cdf[j]	Cumulative dev factor to ultimate; cdf[-1] == tail
latest_diagonal[i]	Most recent observed value per AY
ultimates[i]	Projected ultimate per AY
ibnr[i]	Ultimate − Latest per AY
total_*	Sums of the three above
n_acc, n_dev	Triangle dimensions

mack_stochastic(triangle, selected_factors, excluded?)

Mack (1993) distribution-free standard errors. Returns σ̂²_j per dev period (tail-rule backfilled when only one observation), SE & CV per row, and SE_total / CV_total including cross-row covariance per eq. 5.15.

mack_diagnostics(triangle, selected_factors, excluded?, outlier_threshold?)

Returns standardised residuals, outliers (|r| > threshold, default 2.0), the calendar-year sign test, Spearman independence test across adjacent dev columns, and the inflation slope of mean log-link-ratio against accident-year index.

p-value bands to translate to plain English:

p < 0.005 → strong evidence
p < 0.05  → significant
p < 0.10  → borderline
p ≥ 0.10  → no evidence

parse_csv_triangle(path)

Reads a CSV from disk, treats blank / NA / NaN / N/A / − cells as unobserved, strips embedded thousand-separator commas, and skips header / metadata rows. Returns the triangle + dimensions + the absolute path read (useful for the assistant to confirm what it loaded).

project_triangle(triangle, selected_factors)

Fills the lower-right of the triangle with chain-ladder projections. Returns a fully-rectangular list[list[float]] (no nulls). NaN where a row has no observation to project forward from.

to_incremental(cumulative) / to_cumulative(incremental)

Two conversions. Unobserved cells stay unobserved; the inverse on observed cells is exact.

sample_triangle()

Returns the textbook 10×10 cumulative paid triangle. Use as a self-check: pass it to compute_chain_ladder and you should get Paid 49,458 / Ultimate 65,883 / IBNR 16,425.

Triangle format

[
    # AY 1 — fully developed
    [1000, 1855, 2423, 2988, 3335, 3483, 3552, 3603, 3624, 3631],
    # AY 2 — observed through dev 9
    [1113, 2103, 2774, 3422, 3844, 4010, 4090, 4148, 4172, None],
    # …
    # AY 10 — only the first observation
    [2640, None, None, None, None, None, None, None, None, None]
]

• Outer index = accident year, oldest first
• Inner index = development period, 0 = first age
• Use None (or JSON null) for unobserved cells
• All rows must be the same length — pad with trailing null

Testing

pipx install --editable mcp-chainladder
pytest -q

Tests pin every public tool against the textbook triangle's well-known parity values to ~1e-9.

Pro tier

The free tier covers all 8 tools listed above. Pro unlocks additional methods + bulk workflows, gated by a local license file at ~/.chainladder/license (or wherever $CHAINLADDER_LICENSE_FILE points).

Pro licenses are currently for internal and testing purposes only — not open to the public. No purchase channel is available at this time. The Pro tools are listed below for reference and will continue to return pro_license_required for external users.

Pro tool	What it does
pro_license_status	Inspect current license state (free to call)
interpret_diagnostics	Mack tests with verdict labels + plain-English summaries + recommended actions
sensitivity_analysis	Drop each link ratio one-at-a-time and rank by IBNR impact
tail_extrapolation	Fit exponential + inverse-power tail models, recommend best fit
bornhuetter_ferguson	BF reserving method with side-by-side CL comparison
compare_methods	Run CL + BF in one call, report deltas + largest divergence
generate_pdf_report (coming v1.2)	Full 5-page actuarial PDF — cover / triangle / factors / results / 3D loss surface
batch_csv_processing (coming v1.2)	Fold the chain ladder over a directory of CSV triangles
cape_cod, mack_bf (coming v1.3)	Additional reserving methods, all returning side-by-side comparisons

License file format

{
  "product":  "mcp-chainladder-pro",
  "owner":    "[e-mail]",
  "expires":  null,
  "key":      "CL-PRO-1A2B3C4D",
  "signature": "…"
}

Drop it at ~/.chainladder/license (the file's directory must exist; the server doesn't create it). Pro tools immediately respond as unlocked the next time Claude calls them.

When the license is missing or expired, every Pro tool returns {"error": "pro_license_required", "status": {...}} instead of computing — Claude reads the status and points you to the upgrade URL. The free-tier tools always work regardless of license state.

License

MIT. See LICENSE.

Built on top of the open-source Chain Ladder for macOS app, which exposes the same math through a native UI.