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Scenariosim MCP

Deterministic what-if & scenario simulation for AI agents: projections, sensitivity & break-even.

Connector by inity13 · GitHub account verified by the MCP Registry

IndexedMITLocal (stdio)Remote (HTTP)★ 1
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Install

Works with Claude Code, VS Code, Codex, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/inity13-scenariosim-mcp

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Remote endpoint

  • https://scenariosim-mcp.pages.dev/mcp Remote (HTTP)

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About

Deterministic what-if & scenario simulation for AI agents: projections, sensitivity & break-even.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 26, 2026.

Tools
2 read · 1 write · 0 delete · 3 not yet classified
Secrets
None asked for
Install scripts
None found
Runs as
Node.js package

Tools by what they do 6

From the running server's own tool list (tools/list) on Oct 25, 2026.

Read 2 · Look things up without changing anything.

  • health_check
  • list_templates

Write 1 · Create, change, send or run things.

  • run_scenario

Not yet classified 3 · We could not tell from the name and description.

  • break_even
  • compare_scenarios
  • sensitivity_analysis

Network hosts it talks to 4

  • api.stripe.com · found in code
  • github.com · found in code
  • glama.ai · found in code
  • scenariosim-mcp.pages.dev · found in remote endpoint, install command, code

Tools (6)

As the server listed them on Oct 25, 2026. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • 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_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_check

    Server health, version, and capabilities (tools, templates, period labels, max horizon). No parameters.

  • 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.

  • 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_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.

Signals

Stars
1
Forks
0
Open issues
1
Last commit
Oct 20, 2026 (17 days ago)
Last release
Aug 12, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Registry name
io.github.inity13/scenariosim-mcp
Version
1.0.2
Auth
Auth not stated
Packages
npm scenariosim-mcp
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Scenariosim MCP. Agent Mart. https://agentmart.dev/connectors/inity13-scenariosim-mcp (last updated 2026-10-24)