io.github.Kromatic-Innovation/neumann · v0.1.3 · finance-and-payments
io.github.Kromatic-Innovation/neumann is healthy: it completed an MCP handshake in 911 ms and exposes 10 tools.
Turns a guessed number into a sampled distribution — percentiles, NPV/IRR/payback, sensitivity.
Endpoint: https://neumann.kromatic.com/mcp (streamable-http)
Repository · JSON · Add to my inventory
| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 10 |
| Latency | 911 ms |
| Last checked (UTC) | 2026-10-28T12:35:13.344Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-28T12:35:13.344Z | Healthy | 911 |
run_simulation | Runs a Monte Carlo simulation over a graph of probabilistic root and calculated nodes, returning the full output distribution instead of one guessed number. Use this when you need the range and likelihood of outcomes for a plan driven by uncertain inputs, not just its average case. Both tiers run to |
oat_sensitivity | Runs a one-at-a-time tornado sensitivity analysis: sweeps each root node over a set of quantiles (others held at baseline) and measures how the target node responds, ranked by impact. Use this when you need to know which uncertain input most drives an outcome, so you know what to de-risk or estimate |
scenario_analysis | Runs a simulation once and summarises a target node's worst/median/best outcomes (any percentiles), each paired with a best-effort driving input per root node. Use this when you need a quick worst-case/median/best-case narrative for a plan rather than the full distribution. Debits one credit for aut |
validate_graph | Checks a graph for structural problems (cycles, dangling edges, unknown distributions, missing refs) before you run it, returning a list of issues by rule and severity. Use this when you built or edited a graph by hand and want to catch mistakes before spending a run on it. Free; no credits debited. |
estimate | Pre-flight-sizes a graph without running it: estimated peak memory, engine complexity, which tier (small/large) it would run as, and the credit cost. Use this when you want to know what a run will cost or how heavy it is before committing to it, especially for a large or unfamiliar graph. Free; no c |
solve_distribution_parameters | Converts a human-style estimate (a min/max range at a stated confidence, or a most-likely value) into the distribution_params a root node needs. Use this when you have a rough verbal estimate like 'between 80 and 120, 90% confident' and need engine-ready parameters rather than guessing them yourself |
get_results | Fetches the status and, once available, the summarised result for a previously-submitted run by its run_id. Use this when you want to re-check or re-fetch a run you already submitted (e.g. via run_simulation) without resubmitting the graph -- run_simulation itself already blocks until done and retur |
feedback | Sends feedback, a feature request, or a bug report straight to the Neumann team, CC'ing your sender_email so you keep a copy. Use this when something about the service is wrong, missing, or confusing and you want a human to see it directly rather than guessing at a workaround. Anonymous submissions |
buy_credits | Purchases credits using a payment method already saved from a prior Stripe Checkout session. Use this when an authenticated account is running low on credits and needs more to keep submitting runs or analyses, without a human re-entering card details. See GET /billing/pricing for the current per-cre |
share_result | Creates (or fetches the existing) public share link for a PERSISTED run from the stored-graph flow, renderable as HTML/JSON/SVG. Use this when you want to hand a result to someone without giving them API access -- only for a run saved via the stored-graph flow: a run_simulation result is ephemeral a |