io.github.inity13由 GitHub 账号 inity13 发布v1.0.1更新于 Oct 27, 2026
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
确定性的推理工具集，让助手运行假设情景模拟、按加权标准给选项排序，并精确计算财务与业务指标。
Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。
把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。
阅读 https://sourceweft.com/mcp/io.github.inity13%2Fagentstack-mcp/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。把它添加到你客户端的 mcpServers 配置中。
{
"mcpServers": {
"agentstack": {
"type": "http",
"url": "https://agentstack-mcp.pages.dev/mcp"
}
}
}One deterministic reasoning stack for AI agents — behind a single Model Context Protocol (MCP) endpoint and one API key:
simulate (ScenarioSim) → decide (DecisionMatrix) → compute (PrecisionCalc)
plus cross-domain composite tools that chain the three. Every number runs
through decimal.js at 40-digit precision
(never floats), so identical inputs always produce byte-identical output.
The server is stateless — no database, no sessions.
AgentStack bundles three standalone MCP servers so an agent installs one server, uses one key, and can run reasoning that spans all three domains. It imports the same engines directly (no HTTP proxying) — zero added latency, no cascading failure.
Free tier: no key, 20 calls/day per IP. Paid plans (Starter $19/mo · 8,000/day,
Pro $59/mo · 80,000/day) return an API key sent as X-API-Key. One key covers
all three products and the composites — about half the cost of subscribing to them
separately. Landing page: https://agentstack-mcp.pages.dev.
Note on two deliberately-namespaced collisions:
sim_sensitivityvaries scenario inputs;decide_sensitivityvaries criteria weights. They are different operations — the prefix disambiguates them for the agent.
Deterministic end-to-end; each chains engines that no single server bundles:
Composite responses report the pipeline they ran and carry each stage's
structured output, so an agent can drill into any step.
Append ?profile= to the endpoint to load only a subset:
Namespaced tools return the exact envelope of their underlying product. Composite
tools add composite, pipeline, per-stage blocks, methodology, notes, and a
plain-language explanation. Errors are always structured data:
Example — plan_to_valuation (trimmed):
Why vendor the engines? So this repo is self-contained and reproducible. The
engines are the same deterministic modules that power the three standalone
servers; npm run sync-engines re-copies them from the sibling repos when an
upstream change lands.
Quick manual call:
Run the server locally over stdio with a single command — nothing to deploy:
Claude Desktop / any stdio MCP client (claude_desktop_config.json):
This is the same deterministic engine as the hosted server, running on your machine.
~/.cursor/mcp.jsonclaude_desktop_config.json (via mcp-remote).vscode/mcp.json~/.codeium/windsurf/mcp_config.jsonAdd "headers": { "X-API-Key": "ssak_live_..." } once you have a key.
Rank growth strategies on their projected outcomes:
Value a plan's MRR stream:
Stress-test the decision:
Runs fully free / private with no bindings or secrets (fails open on the free
tier). For billing: bind a KV namespace AGENTSTACK_KV, set PRICE_STARTER /
PRICE_PRO in [vars], and add Stripe secrets:
Routes: /mcp (Streamable HTTP), /checkout, /success, /portal, /webhook, /metrics.
Enforced in worker-src/billing.mjs: identify() reads X-API-Key /
Authorization: Bearer and falls back to per-IP free tier; consumeQuota() is a
KV daily counter (the single gating point). One key / one quota spans the whole
stack. Marked NOTE (auth) / NOTE (rate limiting) seams show where to swap in
JWT/mTLS or a token-bucket. None of it touches the deterministic math.
AgentStack is additive, not a replacement. The three products keep running on their own for single-domain use:
MIT — see LICENSE.
来源：README.md，提交 7f659dd
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.
无需输入参数。
Aggregated health/status for the whole stack (all three engines + composites). No parameters.
无需输入参数。
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.
inputshorizonmetricstemplateperiod_labelSIMULATE. 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').
maxminstepsinputsvalueshorizontemplate必填variablevariablesvariationperiod_labeltarget_metricSIMULATE. Solve for the scenario input value required to make an output metric hit a target value (deterministic bisection). Requires 'template', 'solve_for', 'target_value'.
boundsinputshorizontemplate必填solve_for必填period_labeltarget_value必填target_metricSIMULATE. 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.
goalhorizonscenarios必填compare_metricinclude_projectionsSIMULATE. List every scenario template (inputs, defaults, outputs) plus the custom-model format and period labels. No parameters.
无需输入参数。
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.
methodscores必填options必填criteria必填DECIDE. Return the full normalized scored matrix (per-option, per-criterion) + ranking when scores are supplied separately, without the winner narrative.
methodscores必填options必填criteria必填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.
stepsmethodscores必填options必填criteria必填variationDECIDE. 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.
methodscores必填optionscriteria必填option_aoption_bDECIDE. List the scoring methods (weighted_sum, weighted_product, topsis) with normalization details and when to use each. No parameters.
无需输入参数。
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.
metric必填params必填currencyCOMPUTE. List every supported metric with descriptions and required/optional params. No parameters.
无需输入参数。
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.
dateliveamount必填to_currency必填from_currency必填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.
daysregionend_dateoperation必填start_date必填custom_holidaysCOMPUTE. Compound-interest/growth math. operation: future_value | present_value | cagr. rate is annual decimal; compounding: daily|weekly|monthly|quarterly|semiannually|annually|continuous.
rateyearscurrencyend_valueoperation必填begin_valuecompoundingfuture_valuepresent_valueCOMPUTE. Net Present Value (discounted cash flow). NPV = sum(CF_t/(1+rate)^t); cashflows[0] is period 0 (usually the negative outlay).
rate必填currencycashflows必填COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.
guesscashflows必填COMPUTE. Level-payment loan: monthly payment, total interest, payoff, and (optional) full schedule.
currencyprincipal必填annual_rate必填term_months必填extra_paymentinclude_scheduleCOMPUTE. Asset depreciation schedule. method: straight_line | declining_balance | sum_of_years_digits.
cost必填method必填currencysalvage_value必填useful_life_years必填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.
rate必填inputshorizonmetricscurrencytemplate必填period_labelcashflow_metric必填initial_investmentCOMPOSITE (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.
inputsmethodhorizonoptions必填criteria必填templateperiod_labelCOMPOSITE (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}.
inputsmethodstress必填horizonoptions必填criteria必填templateperiod_label已满足适用检查项的 100%
该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。
https://agentstack-mcp.pages.dev/mcp{ "mcpServers": { "agentstack": { "type": "http", "url": "https://agentstack-mcp.pages.dev/mcp" } } }{ "mcpServers": { "agentstack": { "url": "https://agentstack-mcp.pages.dev/mcp?profile=finance" } } }{ "status": "error", "error": { "type": "unknown_metric", "message": "...", "hint": "..." } }{ "status": "success", "composite": "plan_to_valuation", "pipeline": ["scenariosim.run_scenario", "precisioncalc.net_present_value", "precisioncalc.internal_rate_of_return"], "scenario": { "template": "saas_growth", "horizon": 12, "key_results": { "ending_mrr": 26982.1 } }, "cashflow_metric": "mrr", "cashflows": [-150000, 14040, 15600, "..."], "valuation": { "discount_rate": 0.01, "npv": 11157.386023, "irr": 0.02504, "value_creating": true }, "explanation": "Projecting 'saas_growth' over 12 month(s) and valuing the 'mrr' stream at 1%/period gives an NPV of ..."}agentstack-mcp/├── worker-src/│ ├── index.mjs # Cloudflare Pages Function: MCP transport, namespacing, profiles, billing│ ├── composites.mjs # the 3 cross-domain tools (simulate -> decide -> compute)│ ├── billing.mjs # Stripe + KV: one key / one quota for the whole stack│ └── engines/ # vendored, unmodified product engines (100% deterministic)│ ├── scenariosim.mjs│ ├── decisionmatrix.mjs│ └── precisioncalc.mjs├── server.mjs # local stdio MCP server (same engines + composites; AGENTSTACK_PROFILE env)├── site/ # landing page + mcp.json + llms.txt + _worker.js (built)├── tests/composites.test.mjs├── examples/agent_example.mjs├── scripts/sync-engines.sh # re-vendor engines from the sibling repos├── package.json · wrangler.toml · server.json · smithery.yaml · Dockerfile · LICENSE└── README.mdgit clone <your-fork> agentstack-mcp && cd agentstack-mcpnpm installnpm test # composite-logic tests (no network)npm run dev # -> http://127.0.0.1:8788/mcpnode examples/agent_example.mjs # hosted demonode examples/agent_example.mjs http://127.0.0.1:8788node server.mjs # dependency-light stdio server (AGENTSTACK_PROFILE=finance to subset)curl -s http://127.0.0.1:8788/mcp \ -H 'content-type: application/json' -H 'accept: application/json, text/event-stream' \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_capabilities","arguments":{}}}'npx -y agentstack-mcp{ "mcpServers": { "agentstack": { "command": "npx", "args": ["-y", "agentstack-mcp"] } } }{ "mcpServers": { "agentstack": { "url": "https://agentstack-mcp.pages.dev/mcp" } } }{ "mcpServers": { "agentstack": { "command": "npx", "args": ["-y", "mcp-remote", "https://agentstack-mcp.pages.dev/mcp"] } } }{ "servers": { "agentstack": { "type": "http", "url": "https://agentstack-mcp.pages.dev/mcp" } } }{ "mcpServers": { "agentstack": { "serverUrl": "https://agentstack-mcp.pages.dev/mcp" } } }{ "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" } ]} }{ "name": "plan_to_valuation", "arguments": { "template": "saas_growth", "horizon": 12, "inputs": { "new_customers_per_period": 60, "churn_rate": 0.05, "arpu": 60 }, "cashflow_metric": "mrr", "rate": 0.01, "initial_investment": 150000} }{ "name": "stress_test_decision", "arguments": { "template": "saas_growth", "horizon": 12, "options": [ { "name": "Aggressive", "inputs": { "churn_rate": 0.05 } }, { "name": "Lean", "inputs": { "churn_rate": 0.02 } } ], "criteria": [ { "metric": "ending_mrr", "weight": 3 } ], "stress": { "variable": "churn_rate", "variation": 0.5, "steps": 5 }} }npm installnpx wrangler loginnpm run deploy # esbuild -> site/_worker.js, then wrangler pages deploywrangler pages secret put STRIPE_SECRET_KEY --project-name agentstack-mcpwrangler pages secret put STRIPE_WEBHOOK_SECRET --project-name agentstack-mcp