AgentStack MCP: one deterministic reasoning stack for AI agents (simulate + decide + compute)

The fourth in a suite of deterministic MCP servers for AI agents — and the one that ties the first three together. Over the last stretch I shipped three focused, deterministic MCP servers: ScenarioSim — what-if / scenario simulation DecisionMatrix — multi-criteria decision analysis PrecisionCalc — exact finance / business math They're great on their own, but agents kept needing all three in the same task — and installing three servers, juggling three keys, and hand-gluing their outputs is friction. So here's AgentStack MCP : one endpoint, one key, all three — plus composite tools that chain them. simulate → decide → compute { "mcpServers" : { "agentstack" : { "type" : "http" , "url" : "https://agentstack-mcp.pages.dev/mcp" } } } Free tier: no key, 20 calls/day. The tools are namespaced so an agent always knows which engine it's calling: sim_* — ScenarioSim (run, sensitivity, break-even, compare, templates) decide_* — DecisionMatrix (decide, score, sensitivity, compare_two, methods) calc_* — PrecisionCalc (metrics, currency, NPV, IRR, loan, depreciation, …) The part that's actually new: composite tools These chain the engines to do reasoning no single server can , deterministically end-to-end: evaluate_options_with_scenarios (simulate → decide) — project each option as its own scenario, then rank the outcomes against weighted criteria: { "name" : "evaluate_options_with_scenarios" , "arguments" : { "template" : "saas_growth" , "horizon" : 12 , "options" : [ { "name" : "Aggressive" , "inputs" : { "new_customers_per_period" : 60 , "churn_rate" : 0.05 } }, { "name" : "Lean" , "inputs" : { "new_customers_per_period" : 20 , "churn_rate" : 0.02 } } ], "criteria" : [ { "metric" : "ending_mrr" , "weight" : 3 , "direction" : "benefit" }, { "metric" : "total_churned_customers" , "weight" : 1 , "direction" : "cost" } ] } } plan_to_valuation (simulate → compute) — project a plan, then value its cash-flow line: NPV, IRR, undiscounted total. stress_test_decision (simulate × decide)