{"name":"io.github.datalattice/mcp-chainladder","slug":"datalattice-mcp-chainladder","title":null,"description":"Actuarial chain-ladder reserving: IBNR, Mack stochastic stats, and assumption diagnostics.","url":"https://mcp.market/server/datalattice-mcp-chainladder","rating":null,"grade":"B","score":78,"certified":false,"status":"active","category":"other","tags":[],"presence":{"score":13,"stars":0,"forks":0,"downloads_week":20,"last_push_at":"2026-05-14T22:23:27.000Z","license":"NOASSERTION"},"uptime":null,"claimed":false,"transport":"pypi","callable_via_gateway":false,"default_price_micros":0,"repository":"https://github.com/datalattice/mcp-chainladder","website":null,"version":"1.2.2","remotes":[],"packages":[{"registryType":"pypi","registryBaseUrl":"https://pypi.org","identifier":"mcp-chainladder","version":"1.2.2","runtimeHint":"uvx","transport":{"type":"stdio"}}],"tools":[{"name":"bornhuetter_ferguson","description":"Bornhuetter-Ferguson (1972) reserving method. **Pro tier.**","write_action":false,"price_micros":0,"input_schema":null},{"name":"compare_methods","description":"Run chain ladder + Bornhuetter-Ferguson on the same triangle and return a side-by-side comparison. **Pro tier.**","write_action":true,"price_micros":0,"input_schema":null},{"name":"compute_chain_ladder","description":"Run a full chain-ladder reserving calculation on a cumulative loss triangle. This is the workhorse tool — call it whenever the user asks \"what's the IBNR?\", \"what does the chain ladder say?\", or hands you a triangle and asks for projections.","write_action":true,"price_micros":0,"input_schema":null},{"name":"interpret_diagnostics","description":"Run Mack assumption diagnostics + label each result with a plain- English verdict and a recommended action. **Pro tier.**","write_action":true,"price_micros":0,"input_schema":null},{"name":"mack_diagnostics","description":"Mack (1994) assumption diagnostics — three statistical tests plus standardised-residual outlier detection. Use when the user asks \"does the chain ladder look OK?\", \"are there any outliers?\", or \"should I be worried about [calendar-year / inflation / dependence] effects?\".","write_action":false,"price_micros":0,"input_schema":null},{"name":"mack_stochastic","description":"Mack (1993) stochastic chain-ladder error estimation. Returns distribution-free standard errors and coefficients of variation per accident row plus the totals — the canonical sensitivity check for a deterministic chain-ladder result.","write_action":false,"price_micros":0,"input_schema":null},{"name":"parse_csv_triangle","description":"Parse a CSV file from disk into a cumulative loss triangle.","write_action":false,"price_micros":0,"input_schema":null},{"name":"pro_license_status","description":"Inspect the current Pro-tier license state. Returns whether Pro tools are unlocked, the registered owner, an expiry date if any, and a human-readable message that Claude can relay to the user. Free to call regardless of license state.","write_action":false,"price_micros":0,"input_schema":null},{"name":"project_triangle","description":"Fill the lower-right (unobserved) cells of a cumulative triangle using the supplied age-to-age factors.","write_action":false,"price_micros":0,"input_schema":null},{"name":"sample_triangle","description":"Return the classic textbook 10×10 cumulative-paid triangle (Friedland-style). Useful for demos, examples, and verifying the server is working — feed it to `compute_chain_ladder` to get the well-known parity values (Paid 49,458 / Ultimate 65,883 / IBNR 16,425 / Mack SE ±354.61).","write_action":false,"price_micros":0,"input_schema":null},{"name":"sensitivity_analysis","description":"Drop each observable link ratio one at a time, rerun the chain ladder, and rank the link ratios by their impact on total IBNR. **Pro tier.**","write_action":true,"price_micros":0,"input_schema":null},{"name":"tail_extrapolation","description":"Fit parametric tail models to the late development factors and extrapolate forward. **Pro tier.**","write_action":false,"price_micros":0,"input_schema":null},{"name":"to_cumulative","description":"Convert an incremental triangle to cumulative (running sum per row). Unobserved cells propagate as null. Inverse of `to_incremental` on observed cells.","write_action":false,"price_micros":0,"input_schema":null},{"name":"to_incremental","description":"Convert a cumulative triangle to incremental (per-period) values.","write_action":false,"price_micros":0,"input_schema":null}],"scan":{"score":78,"grade":"B","scanned_at":"2026-09-27T21:29:26.284Z","report":{"scannerVersion":"0.1.9","scannedAt":"2026-09-27T21:29:26.265Z","components":{"code":{"score":25,"max":25,"notes":["10 source files scanned"]},"reliability":{"score":-1,"max":20,"notes":["no gateway calls yet and no remote to probe"]},"poisoning":{"score":-1,"max":15,"notes":["tools not inspected (local package is not executed); not counted"]},"auth":{"score":12,"max":15,"notes":["local package, no credentials required"]},"maintenance":{"score":8,"max":15,"notes":["last push 136 days ago"]},"identity":{"score":6,"max":10,"notes":["registry namespace matches repository owner"]}},"findings":[],"inputs":{"packages":[{"registryType":"pypi","identifier":"mcp-chainladder","version":"1.2.2","found":true,"weeklyDownloads":20,"license":"MIT","dependencyCount":3,"publishedAt":"2026-05-13T01:18:57.506858Z","repositoryUrl":"https://github.com/datalattice/mcp-chainladder"}],"repo":{"found":true,"owner":"datalattice","repo":"mcp-chainladder","archived":false,"pushedAt":"2026-05-14T22:23:27Z","stars":0,"forks":0,"openIssues":0,"ownerType":"User","ownerAvatarUrl":"https://avatars.githubusercontent.com/u/284125585?v=4","ownerCreatedAt":"2026-05-12T20:50:19Z","license":"NOASSERTION"},"icon":{"url":null,"source":"none"},"presence":{"stars":0,"forks":0,"downloadsWeek":20,"license":"NOASSERTION","lastPushAt":"2026-05-14T22:23:27.000Z","score":13}}}},"grade_history":[],"reviews":[]}