_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) — stress one scenario assumption across every option and report how often the chosen option survives (robustness) and where it flips. ## Fighting tool bloat with profiles Bundling 24 tools risks drowning an agent's tool-selection. So the endpoint takes a `?profile=` filter: https://agentstack-mcp.pages.dev/mcp?profile=finance * `finance` → `calc_*` + `plan_to_valuation` * `decision` → `decide_*` + the two decision composites * `simulation` → `sim_*` + all composites * `all` (default) → everything ## Why it's built this way * **Deterministic** : everything runs through decimal.js at 40-digit precision. Same inputs → byte-identical output, across all three engines and the composites. * **No proxying** : AgentStack imports the _same_ engines directly, so there's zero added latency and no cascading failure — not three network hops behind one URL. * **Additive, not a replacement** : the three standalone servers keep running for single-domain use. * **One key, one quota** : ~half the price of subscribing to the three separately. * **Stateless + MIT** : self-host on Cloudflare Pages, Node, Deno, or Bun. ## Links * **Live endpoint:** https://agentstack-mcp.pages.dev/mcp * **Site + docs:** https://agentstack-mcp.pages.dev * **GitHub (MIT):** https://github.com/inity13/agentstack-mcp * **MCP Registry:** `io.github.inity13/agentstack-mcp` If your agents plan, choose, and do the numbers, give them one calculator that does all three — and never drifts. Feedback welcome.