agentstack-mcp
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
我該用這個嗎
品質與安全性
發現項目（2）
- LOWTool 'decide_score' description lacks action verb在 decide_score 中
- LOWTool 'evaluate_options_with_scenarios' name length outside 3-30 range在 evaluate_options_with_scenarios 中
根據工具定義與協定合規性的自動化分析。
上下文成本
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高，可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案：
{
"mcpServers": {
"agentstack-mcp": {
"command": "npx",
"args": [
"agentstack-mcp"
]
}
}
}可執行的套件
1.0.1stdio遠端端點
https://agentstack-mcp.pages.dev/mcpstreamable-http它能做什麼
工具清單
工具（24）
🟢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.
輸入結構描述
{
"type": "object",
"properties": {}
}🟢health_check
Aggregated health/status for the whole stack (all three engines + composites). No parameters.
輸入結構描述
{
"type": "object",
"properties": {}
}⚪sim_run(template, inputs, metrics, horizon, period_label)
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.
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"metrics": {
"type": "array",
"items": {
"type": "object"
}
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
}
}
}🟢sim_sensitivity(template, inputs, target_metric, variable, variables, ...)
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').
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"target_metric": {
"type": "string"
},
"variable": {
"type": "string"
},
"variables": {
"type": "array",
"items": {
"type": "object"
}
},
"variation": {
"type": "number"
},
"steps": {
"type": "integer"
},
"values": {
"type": "array",
"items": {
"type": "number"
}
},
"min": {
"type": "number"
},
"max": {
"type": "number"
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
}
},
"required": [
"template"
]
}⚪sim_break_even(template, inputs, solve_for, target_metric, target_value, ...)
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'.
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"solve_for": {
"type": "string"
},
"target_metric": {
"type": "string"
},
"target_value": {
"type": "number"
},
"bounds": {
"type": "array",
"items": {
"type": "number"
}
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
}
},
"required": [
"template",
"solve_for",
"target_value"
]
}⚪sim_compare(scenarios, compare_metric, goal, horizon, include_projections)
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.
輸入結構描述
{
"type": "object",
"properties": {
"scenarios": {
"type": "array",
"items": {
"type": "object"
}
},
"compare_metric": {
"type": "string"
},
"goal": {
"type": "string"
},
"horizon": {
"type": "integer"
},
"include_projections": {
"type": "boolean"
}
},
"required": [
"scenarios"
]
}🟢sim_list_templates
SIMULATE. List every scenario template (inputs, defaults, outputs) plus the custom-model format and period labels. No parameters.
輸入結構描述
{
"type": "object",
"properties": {}
}⚪decide(options, criteria, scores, method)
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.
輸入結構描述
{
"type": "object",
"properties": {
"options": {
"type": "array"
},
"criteria": {
"type": "array"
},
"scores": {
"type": "object"
},
"method": {
"type": "string"
}
},
"required": [
"options",
"criteria",
"scores"
]
}⚪decide_score(options, criteria, scores, method)
DECIDE. Return the full normalized scored matrix (per-option, per-criterion) + ranking when scores are supplied separately, without the winner narrative.
輸入結構描述
{
"type": "object",
"properties": {
"options": {
"type": "array"
},
"criteria": {
"type": "array"
},
"scores": {
"type": "object"
},
"method": {
"type": "string"
}
},
"required": [
"options",
"criteria",
"scores"
]
}⚪decide_sensitivity(options, criteria, scores, method, variation, ...)
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.
輸入結構描述
{
"type": "object",
"properties": {
"options": {
"type": "array"
},
"criteria": {
"type": "array"
},
"scores": {
"type": "object"
},
"method": {
"type": "string"
},
"variation": {
"type": "number"
},
"steps": {
"type": "integer"
}
},
"required": [
"options",
"criteria",
"scores"
]
}⚪decide_compare_two(option_a, option_b, options, criteria, scores, ...)
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.
輸入結構描述
{
"type": "object",
"properties": {
"option_a": {
"type": "string"
},
"option_b": {
"type": "string"
},
"options": {
"type": "array"
},
"criteria": {
"type": "array"
},
"scores": {
"type": "object"
},
"method": {
"type": "string"
}
},
"required": [
"criteria",
"scores"
]
}🟢decide_list_methods
DECIDE. List the scoring methods (weighted_sum, weighted_product, topsis) with normalization details and when to use each. No parameters.
輸入結構描述
{
"type": "object",
"properties": {}
}🟢calc_metric(metric, params, currency)
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.
輸入結構描述
{
"type": "object",
"properties": {
"metric": {
"type": "string"
},
"params": {
"type": "object"
},
"currency": {
"type": "string"
}
},
"required": [
"metric",
"params"
]
}🟢calc_list_metrics
COMPUTE. List every supported metric with descriptions and required/optional params. No parameters.
輸入結構描述
{
"type": "object",
"properties": {}
}⚪calc_currency_convert(amount, from_currency, to_currency, date, live)
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.
輸入結構描述
{
"type": "object",
"properties": {
"amount": {
"type": "number"
},
"from_currency": {
"type": "string"
},
"to_currency": {
"type": "string"
},
"date": {
"type": "string"
},
"live": {
"type": "boolean"
}
},
"required": [
"amount",
"from_currency",
"to_currency"
]
}🟡calc_business_days(operation, start_date, days, end_date, region, ...)
COMPUTE. Business-day arithmetic honoring weekends + regional holidays. operation: add_business_days | count_business_days | next_business_day | previous_business_day. region: US | UK | EU | NONE.
輸入結構描述
{
"type": "object",
"properties": {
"operation": {
"type": "string"
},
"start_date": {
"type": "string"
},
"days": {
"type": "integer"
},
"end_date": {
"type": "string"
},
"region": {
"type": "string"
},
"custom_holidays": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"operation",
"start_date"
]
}⚪calc_compound_growth(operation, rate, years, present_value, future_value, ...)
COMPUTE. Compound-interest/growth math. operation: future_value | present_value | cagr. rate is annual decimal; compounding: daily|weekly|monthly|quarterly|semiannually|annually|continuous.
輸入結構描述
{
"type": "object",
"properties": {
"operation": {
"type": "string"
},
"rate": {
"type": "number"
},
"years": {
"type": "number"
},
"present_value": {
"type": "number"
},
"future_value": {
"type": "number"
},
"begin_value": {
"type": "number"
},
"end_value": {
"type": "number"
},
"compounding": {
"type": "string"
},
"currency": {
"type": "string"
}
},
"required": [
"operation"
]
}⚪calc_npv(rate, cashflows, currency)
COMPUTE. Net Present Value (discounted cash flow). NPV = sum(CF_t/(1+rate)^t); cashflows[0] is period 0 (usually the negative outlay).
輸入結構描述
{
"type": "object",
"properties": {
"rate": {
"type": "number"
},
"cashflows": {
"type": "array",
"items": {
"type": "number"
}
},
"currency": {
"type": "string"
}
},
"required": [
"rate",
"cashflows"
]
}🟡calc_irr(cashflows, guess)
COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.
輸入結構描述
{
"type": "object",
"properties": {
"cashflows": {
"type": "array",
"items": {
"type": "number"
}
},
"guess": {
"type": "number"
}
},
"required": [
"cashflows"
]
}⚪calc_loan_amortization(principal, annual_rate, term_months, extra_payment, currency, ...)
COMPUTE. Level-payment loan: monthly payment, total interest, payoff, and (optional) full schedule.
輸入結構描述
{
"type": "object",
"properties": {
"principal": {
"type": "number"
},
"annual_rate": {
"type": "number"
},
"term_months": {
"type": "integer"
},
"extra_payment": {
"type": "number"
},
"currency": {
"type": "string"
},
"include_schedule": {
"type": "boolean"
}
},
"required": [
"principal",
"annual_rate",
"term_months"
]
}⚪calc_depreciation(method, cost, salvage_value, useful_life_years, currency)
COMPUTE. Asset depreciation schedule. method: straight_line | declining_balance | sum_of_years_digits.
輸入結構描述
{
"type": "object",
"properties": {
"method": {
"type": "string"
},
"cost": {
"type": "number"
},
"salvage_value": {
"type": "number"
},
"useful_life_years": {
"type": "integer"
},
"currency": {
"type": "string"
}
},
"required": [
"method",
"cost",
"salvage_value",
"useful_life_years"
]
}⚪plan_to_valuation(template, inputs, metrics, horizon, period_label, ...)
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.
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"metrics": {
"type": "array",
"items": {
"type": "object"
}
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
},
"cashflow_metric": {
"type": "string"
},
"rate": {
"type": "number"
},
"initial_investment": {
"type": "number"
},
"currency": {
"type": "string"
}
},
"required": [
"template",
"cashflow_metric",
"rate"
]
}⚪evaluate_options_with_scenarios(template, inputs, horizon, period_label, method, ...)
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.
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
},
"method": {
"type": "string"
},
"options": {
"type": "array",
"items": {
"type": "object"
}
},
"criteria": {
"type": "array",
"items": {
"type": "object"
}
}
},
"required": [
"options",
"criteria"
]
}⚪stress_test_decision(template, inputs, horizon, period_label, method, ...)
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}.
輸入結構描述
{
"type": "object",
"properties": {
"template": {
"type": "string"
},
"inputs": {
"type": "object"
},
"horizon": {
"type": "integer"
},
"period_label": {
"type": "string"
},
"method": {
"type": "string"
},
"options": {
"type": "array",
"items": {
"type": "object"
}
},
"criteria": {
"type": "array",
"items": {
"type": "object"
}
},
"stress": {
"type": "object"
}
},
"required": [
"options",
"criteria",
"stress"
]
}社群
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