Agentstack MCP Server

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answering

Agentstack MCP is answering right now. Last checked 3 min ago. 47 installs a week from npm. It exposes 24 tools. Last commit 12 Aug 2026.

Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.

Installs per day peak 244 · avg 9

a month agotoday

Uptime history 21 days of history · worst day 65%

21 days agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

79 ms

Response time

average over 24h

Installs / week

npm and PyPI

Nothing serious here today

Today is the operative word: we check Agentstack 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 3 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add agentstack-mcp --transport http https://agentstack-mcp.pages.dev/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "agentstack-mcp": {
      "url": "https://agentstack-mcp.pages.dev/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.agentstack-mcp]
url = "https://agentstack-mcp.pages.dev/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "agentstack-mcp": {
      "url": "https://agentstack-mcp.pages.dev/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "agentstack-mcp": {
      "url": "https://agentstack-mcp.pages.dev/mcp"
    }
  }
}

Available tools 24

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

calc

calc_business_days

calc_compound_growth

calc_currency_convert

COMPUTE. Convert between major currencies (USD, EUR, GBP, JPY, CAD, AUD, CHF, CNY, INR) with Decimal precision. Static offline table by default; live/historical ECB rates via date/live=true.

calc_depreciation

COMPUTE. Asset depreciation schedule. method: straight_line | declining_balance | sum_of_years_digits.

calc_irr

COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.

calc_list_metrics

COMPUTE. List every supported metric with descriptions and required/optional params. No parameters.

calc_loan_amortization

COMPUTE. Level-payment loan: monthly payment, total interest, payoff, and (optional) full schedule.

calc_metric

COMPUTE. Exact business/SaaS/finance metric: ltv, cac, ltv_cac_ratio, payback_period_months, contribution_margin, gross_margin, churn_rate, mrr_growth_rate, arr, break_even_units, nrr, grr, rule_of_40, magic_number. Rates/margins are decimals (0.05=5%). Call calc_list_metrics for schemas.

calc_npv

COMPUTE. Net Present Value (discounted cash flow). NPV = sum(CF_t/(1+rate)^t); cashflows[0] is period 0 (usually the negative outlay).

decide

decide

DECIDE. Rank named options against weighted criteria and return the winner, full ranking, per-criterion breakdowns, methodology, weights, and an explanation. Provide options, criteria [{name, weight, direction}], and a scores matrix. method: weighted_sum (default) | weighted_product | topsis.

decide_compare_two

DECIDE. Head-to-head comparison of exactly two options with per-criterion win counts and margin. Pass option_a/option_b (or a 2-element options array), criteria, and scores.

decide_list_methods

DECIDE. List the scoring methods (weighted_sum, weighted_product, topsis) with normalization details and when to use each. No parameters.

decide_score

DECIDE. Return the full normalized scored matrix (per-option, per-criterion) + ranking when scores are supplied separately, without the winner narrative.

decide_sensitivity

DECIDE. Test how robust the decision winner is to changes in CRITERIA WEIGHTS (distinct from sim_sensitivity, which varies scenario inputs). Sweeps each weight +/-variation and reports a robustness score + flip points.

sim

sim_break_even

SIMULATE. Solve for the scenario input value required to make an output metric hit a target value (deterministic bisection). Requires 'template', 'solve_for', 'target_value'.

sim_compare

SIMULATE. Run 2-3 scenarios and compare their key_results side by side with deltas vs the first (baseline). Optional 'compare_metric' + 'goal' (max|min) picks a winner.

sim_list_templates

SIMULATE. List every scenario template (inputs, defaults, outputs) plus the custom-model format and period labels. No parameters.

sim_run

SIMULATE. Deterministic what-if projection from a 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 per-period projections, key_results, assumptions_used, methodology, and an explanation.

sim_sensitivity

SIMULATE. Vary one or more scenario inputs and show the impact on a target output metric (one-at-a-time), with elasticity + most-influential ranking. Requires 'template' and 'variable' (or 'variables').

capabilities

list_capabilities

Discovery: the three namespaces (sim_*, decide_*, calc_*), the cross-domain composite tools, the available ?profile= filters, and links to the standalone servers. Call this first to see everything AgentStack exposes. No parameters.

evaluate

evaluate_options_with_scenarios

COMPOSITE (simulate -> decide). Project each option as its own scenario, then rank the options against weighted criteria drawn from the scenario OUTCOMES. Provide a base 'template', an 'options' array ([{name, inputs}]), and 'criteria' ([{metric, weight, direction}]) where each metric is a scenario key_result. Combines ScenarioSim + DecisionMatrix.

health

health_check

Aggregated health/status for the whole stack (all three engines + composites). No parameters.

plan

plan_to_valuation

COMPOSITE (simulate -> compute). Project a scenario, take a per-period cash-flow line from its projections ('cashflow_metric', e.g. 'mrr' or 'net_burn'), and value it exactly: NPV at a discount 'rate', IRR, and undiscounted total. Optional 'initial_investment' becomes the period-0 outflow (needed for IRR). Combines ScenarioSim + PrecisionCalc.

stress

stress_test_decision

COMPOSITE (simulate x decide). Take an options-vs-scenarios decision and stress ONE scenario assumption across a range applied to every option; report how often the baseline winner survives (robustness) and where it flips. Same args as evaluate_options_with_scenarios plus 'stress': {variable, variation, steps}.

Endpoints

URL	Transport	State	Latency	Checked
https://agentstack-mcp.pages.dev/mcp	streamable-http	answering	132 ms	3 min ago

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Agentstack MCP — questions

Answers built from our own checks of this server.

What can Agentstack MCP do?

It exposes 24 tools, read directly from the server on our last check. Among them: calc_business_days, calc_compound_growth, calc_currency_convert, calc_depreciation, calc_irr, calc_list_metrics and 18 more. 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 →

What is Agentstack MCP mostly used for?

Its tools cluster around calc, sim and decide. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is Agentstack MCP working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 79 ms. The bar chart above shows every period we have measured.

How do I connect Agentstack 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 Agentstack MCP need an API key?

No. Agentstack MCP completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 24 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Agentstack MCP?

It answers our handshake in 79 ms on average, which is faster than 95% 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 Agentstack MCP?

The npm package agentstack-mcp was installed 47 times in the last week. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is Agentstack MCP open source?

Yes — it is published under the MIT licence, written in JavaScript and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.