Scenariosim MCP is answering right now. Last checked 6 min ago. 35 installs a week from npm. It exposes 6 tools. Last commit 7 Oct 2026.
Deterministic what-if & scenario simulation for AI agents: projections, sensitivity & break-even.
Installs per day
peak 9 · avg 5 · -3% w/w
a month agotoday
Uptime history
66 days of history · worst day 65%
66 days agonow
100.0%
Uptime 24h
92 of 92 checks
6
Tools
read from the server
42 ms
Response time
average over 24h
35
Installs / week
npm and PyPI
Nothing serious here today
Today is the operative word: we check Scenariosim MCP every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
Connect this server
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 6 min ago.
run in your terminal
claude mcp add scenariosim-mcp --transport http https://scenariosim-mcp.pages.dev/mcp~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"scenariosim-mcp": {
"url": "https://scenariosim-mcp.pages.dev/mcp"
}
}
}~/.codex/config.toml
[mcp_servers.scenariosim-mcp]
url = "https://scenariosim-mcp.pages.dev/mcp".cursor/mcp.json
{
"mcpServers": {
"scenariosim-mcp": {
"url": "https://scenariosim-mcp.pages.dev/mcp"
}
}
}.vscode/mcp.json
{
"mcpServers": {
"scenariosim-mcp": {
"url": "https://scenariosim-mcp.pages.dev/mcp"
}
}
}Available tools 6
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
break
break_even
Solve for the input value required to make an output metric hit a target value (deterministic bisection root-finding). Provide 'template', 'solve_for' (the input to solve), 'target_metric' (defaults to the primary output), and 'target_value'. Optionally pass 'bounds' [low, high] to constrain the search. Returns the required input value, the change from baseline, the achieved metric, and the residual. Assumes the metric is monotonic in the solved input over the range.
compare
compare_scenarios
Run 2-3 scenarios and compare their key_results side by side, with deltas against the first (baseline) scenario. Provide a 'scenarios' array where each entry is {name?, template, inputs} (each may set its own horizon, or pass a shared top-level 'horizon'). Optionally rank on 'compare_metric' with 'goal' ('max' default | 'min') to pick a winner, and set include_projections:true to also return per-period series.
health
health_check
Server health, version, and capabilities (tools, templates, period labels, max horizon). No parameters.
scenario
run_scenario
Main simulation tool. Run a deterministic what-if projection from a pre-built template (saas_growth, pricing_change, churn_impact, cost_reduction, hiring_plan, cash_runway, unit_economics, marketing_funnel, compound_growth) OR a free-form 'metrics' model. Returns period-by-period projections, headline key_results, the exact assumptions used (with defaults filled in), the methodology, notes, and a plain-language explanation. Pass 'template' + 'inputs' (assumptions), plus optional 'horizon' and 'period_label'. 100% deterministic (40-digit decimal math).
sensitivity
sensitivity_analysis
Vary one or more input assumptions and show the impact on a target output metric (one-at-a-time sensitivity). Provide 'template', the input to sweep via 'variable' (or 'variables' array), and 'target_metric' (defaults to the template's primary output). Control the sweep with 'variation' (fractional +/- around the baseline, default 0.2), 'steps' (default 5), or explicit 'values' / 'min'+'max'. Returns per-variable sweeps, an elasticity estimate, the output range, and a ranking of the most influential inputs.
templates
list_templates
Discovery tool: list every pre-built scenario template (id, label, category, description, primary output, documented inputs with defaults/units, and available output metrics), plus how to run a custom free-form scenario and the supported period labels. No required parameters.
Endpoints
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://scenariosim-mcp.pages.dev/mcp | streamable-http | answering | 31 ms | 6 min ago |
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Scenariosim MCP — questions
Answers built from our own checks of this server.
What can Scenariosim MCP do?
It exposes 6 tools, read directly from the server on our last check. Among them: break_even, compare_scenarios, health_check, list_templates, run_scenario, sensitivity_analysis. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README.
How MCP servers expose tools in the first place →
Is Scenariosim MCP working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 42 ms. The bar chart above shows every period we have measured.
How do I connect Scenariosim MCP?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Scenariosim MCP need an API key?
No. Scenariosim MCP completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 6 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Scenariosim MCP?
It answers our handshake in 42 ms on average, which is faster than 98% of all working MCP servers we measure. That puts it in the quick quarter of the ecosystem. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use Scenariosim MCP?
The npm package scenariosim-mcp was installed 35 times in the last week. Week over week that is -3%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is Scenariosim MCP open source?
Yes — it is published under the MIT licence, written in JavaScript, 1 stars on GitHub and 1 open issue. The source link is on this page, so you can read exactly what it does with your data before you connect it.