Memungkinkan AI agent untuk melakukan penalaran deterministik melalui simulasi skenario, analisis keputusan multi-kriteria, dan komputasi finansial dengan presisi tinggi.
Juga dikenal sebagai inity13/agentstack-mcp
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- Versi terbaru
- 1.0.1
- Commit terakhir
- 20 Okt 2026
- Lisensi
- MIT
Apa itu AgentStack MCP?
AgentStack adalah MCP server. Memungkinkan AI agent untuk melakukan penalaran deterministik melalui simulasi skenario, analisis keputusan multi-kriteria, dan komputasi finansial dengan presisi tinggi. Server ini menyediakan 6 tool, seperti plan_to_valuation, evaluate_options_with_scenarios, stress_test_decision.
Ringkasan dibuat dari README proyek
Tentang AgentStack MCP server
AgentStack MCP menyediakan unified reasoning stack bagi AI agent untuk melakukan simulasi deterministik, analisis keputusan multi-kriteria, dan matematika finansial dengan presisi tinggi. Server ini menggabungkan tiga engine khusus—ScenarioSim, DecisionMatrix, dan PrecisionCalc—ke dalam satu endpoint tunggal. Hal ini memungkinkan agent untuk mengeksekusi pipeline penalaran yang kompleks, seperti memproyeksikan skenario bisnis dan langsung menghitung net present value atau internal rate of return, tanpa latensi dari banyak koneksi terpisah.
Developer dapat menggunakan server ini secara lokal melalui stdio menggunakan npx atau terhubung ke versi hosted melalui HTTP. Layanan hosted menawarkan tier gratis yang dibatasi 20 panggilan per hari per IP, sementara paket berbayar menyediakan kuota harian yang lebih tinggi melalui API key. Sistem ini menggunakan decimal.js untuk menjaga presisi 40-digit, memastikan bahwa output matematika tetap byte-identical untuk input yang sama. Pengguna juga dapat memuat profile spesifik seperti finance, decision, atau simulation untuk membatasi toolset yang tersedia dan mengurangi noise.
Apa yang bisa Anda lakukan dengannya
- 1Memperingkat opsi strategisSeorang agent dapat membandingkan berbagai opsi bisnis dengan mensimulasikan hasilnya dan memperingkatnya berdasarkan kriteria yang telah diberi bobot.
- 2Melakukan stress testingSeorang agent dapat menguji seberapa sensitif suatu keputusan dengan mengubah asumsi tertentu di seluruh opsi yang tersedia.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Cara menginstal AgentStack MCP
Format diverifikasi 10 Nov 2026Instal AgentStack MCP di Claude Code
$ claude mcp add agentstack-mcp -- npx -y agentstack-mcpInstal AgentStack MCP di Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"agentstack-mcp": {
"command": "npx",
"args": [
"-y",
"agentstack-mcp"
]
}
}
}Instal AgentStack MCP di VS Code
.vscode/mcp.json
{
"servers": {
"agentstack-mcp": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"agentstack-mcp"
]
}
}
}Instal AgentStack MCP di Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"agentstack-mcp": {
"command": "npx",
"args": [
"-y",
"agentstack-mcp"
]
}
}
}Instal AgentStack MCP di Codex
~/.codex/config.toml
[mcp_servers.agentstack-mcp] command = "npx" args = ["-y", "agentstack-mcp"]
Instal AgentStack MCP di Gemini CLI
~/.gemini/settings.json
{
"mcpServers": {
"agentstack-mcp": {
"command": "npx",
"args": [
"-y",
"agentstack-mcp"
]
}
}
}Tool AgentStack MCP 6
Terdeteksi dari READMEplan_to_valuationevaluate_options_with_scenariosstress_test_decisionfinancedecisionsimulation
| plan_to_valuation | — | |
| evaluate_options_with_scenarios | — | |
| stress_test_decision | — | |
| finance | — | |
| decision | — | |
| simulation | — |
Pengecekan otomatis
Berjalan 13 Nov 2026Laporan lengkap →Lolos checks otomatis kami. Ini bukan audit keamanan: baca kodenya dan berikan server akses paling terbatas yang dibutuhkannya. Cara kerja checks
- Source repository terhubung dan dapat dijangkauinity13/agentstack-mcp is public and reachablegithub.com/inity13/agentstack-mcp ↗Lolos
- Paket menyebut repo ini sebagai sumbernyaagentstack-mcp names inity13/agentstack-mcp as sourcenpmjs.com/package/agentstack-mcp ↗Lolos
- Provenance build (atestasi Sigstore)No build attestation published for this releaseTidak diperiksa
- Lisensi open source (OSI)MITspdx.org/licenses/MIT.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-10-20github.com/inity13/agentstack-mcp ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerNo popular package one edit away from agentstack-mcpLolos
- Tidak ada advisory kritis di paket maupun dependensinya2 resolved dependencies checked; no critical advisoryosv.dev ↗Lolos
- Tidak ada instruksi tersembunyi untuk model6 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelNo tool named like delete, drop or execLolos
- Endpoint remote menjawab handshake MCPinitialize answered with protocol 2025-06-18 in 67 msLolos
README
Lihat di GitHubAgentStack 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.
🌐 Live hosted server (free, no install)
https://agentstack-mcp.pages.dev/mcp
{ "mcpServers": { "agentstack": {
"type": "http", "url": "https://agentstack-mcp.pages.dev/mcp" } } }
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.
The three namespaces (24 tools)
| namespace | engine | tools |
|---|---|---|
sim_* |
ScenarioSim — what-if / scenario simulation | sim_run, sim_sensitivity, sim_break_even, sim_compare, sim_list_templates |
decide_* |
DecisionMatrix — multi-criteria decision analysis | decide, decide_score, decide_sensitivity, decide_compare_two, decide_list_methods |
calc_* |
PrecisionCalc — exact finance/business math | calc_metric, calc_list_metrics, calc_currency_convert, calc_business_days, calc_compound_growth, calc_npv, calc_irr, calc_loan_amortization, calc_depreciation |
| meta | — | list_capabilities, health_check |
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.
Composite tools — the reason to use the stack
Deterministic end-to-end; each chains engines that no single server bundles:
| Tool | Pipeline | What it does |
|---|---|---|
plan_to_valuation |
simulate → compute | Project a scenario, take a per-period cash-flow line (cashflow_metric), and value it: NPV at a discount rate, IRR, undiscounted total. initial_investment becomes the period-0 outflow (needed for IRR). |
evaluate_options_with_scenarios |
simulate → decide | Project each option as its own scenario, then rank the options against weighted criteria drawn from the scenario outcomes (key_results). |
stress_test_decision |
simulate × decide | Stress one scenario assumption across every option and report how often the baseline winner survives (robustness) and where it flips. |
Composite responses report the pipeline they ran and carry each stage's
structured output, so an agent can drill into any step.
Profiles — fight tool-selection noise
Append ?profile= to the endpoint to load only a subset:
| profile | tools |
|---|---|
all (default) |
every tool (24) |
finance |
calc_* + plan_to_valuation + meta |
decision |
decide_* + evaluate_options_with_scenarios + stress_test_decision + meta |
simulation |
sim_* + all composites + meta |
{ "mcpServers": { "agentstack": {
"url": "https://agentstack-mcp.pages.dev/mcp?profile=finance" } } }
Response envelope
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:
{ "status": "error", "error": { "type": "unknown_metric", "message": "...", "hint": "..." } }
Example — plan_to_valuation (trimmed):
{
"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 ..."
}
Project structure
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.md
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.
Run it locally
git clone <your-fork> agentstack-mcp && cd agentstack-mcp
npm install
npm test # composite-logic tests (no network)
npm run dev # -> http://127.0.0.1:8788/mcp
node examples/agent_example.mjs # hosted demo
node examples/agent_example.mjs http://127.0.0.1:8788
node server.mjs # dependency-light stdio server (AGENTSTACK_PROFILE=finance to subset)
Quick manual call:
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":{}}}'
Install via npm (stdio, no hosting)
Run the server locally over stdio with a single command — nothing to deploy:
npx -y agentstack-mcp
Claude Desktop / any stdio MCP client (claude_desktop_config.json):
{ "mcpServers": { "agentstack": { "command": "npx", "args": ["-y", "agentstack-mcp"] } } }
This is the same deterministic engine as the hosted server, running on your machine.
Client configuration
Cursor — ~/.cursor/mcp.json
{ "mcpServers": { "agentstack": { "url": "https://agentstack-mcp.pages.dev/mcp" } } }
Claude Desktop — claude_desktop_config.json (via mcp-remote)
{ "mcpServers": { "agentstack": { "command": "npx", "args": ["-y", "mcp-remote", "https://agentstack-mcp.pages.dev/mcp"] } } }
VS Code — .vscode/mcp.json
{ "servers": { "agentstack": { "type": "http", "url": "https://agentstack-mcp.pages.dev/mcp" } } }
Windsurf — ~/.codeium/windsurf/mcp_config.json
{ "mcpServers": { "agentstack": { "serverUrl": "https://agentstack-mcp.pages.dev/mcp" } } }
Add "headers": { "X-API-Key": "ssak_live_..." } once you have a key.
Example composite payloads
Rank growth strategies on their projected outcomes:
{ "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" }
]
} }
Value a plan's MRR stream:
{ "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
} }
Stress-test the decision:
{ "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 }
} }
Deploy on Cloudflare Pages
npm install
npx wrangler login
npm run deploy # esbuild -> site/_worker.js, then wrangler pages deploy
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:
wrangler pages secret put STRIPE_SECRET_KEY --project-name agentstack-mcp
wrangler pages secret put STRIPE_WEBHOOK_SECRET --project-name agentstack-mcp
Routes: /mcp (Streamable HTTP), /checkout, /success, /portal, /webhook, /metrics.
Auth & rate limiting
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.
Relationship to the standalone servers
AgentStack is additive, not a replacement. The three products keep running on their own for single-domain use:
- ScenarioSim — https://scenariosim-mcp.pages.dev/mcp
- DecisionMatrix — https://decisionmatrix-mcp.pages.dev/mcp
- PrecisionCalc — https://precisioncalc-mcp.pages.dev/mcp
License
MIT — see LICENSE.
Pertanyaan tentang AgentStack MCP
Bagaimana cara menambahkan AgentStack MCP ke Claude Code?
Jalankan claude mcp add agentstack-mcp -- npx -y agentstack-mcp di terminal Anda. Tambahkan --scope user agar tersedia di semua proyek.
Bagaimana cara menambahkan AgentStack MCP ke Cursor?
Buka ~/.cursor/mcp.json dan tambahkan server di bawah mcpServers, menggunakan config Cursor yang ditunjukkan di bagian instalasi di atas.
Tool apa yang disediakan AgentStack MCP?
Server ini menyediakan 6 tool, termasuk plan_to_valuation, evaluate_options_with_scenarios, stress_test_decision, finance, decision.
Apakah server menyimpan data atau sesi pengguna?
Tidak, server ini bersifat stateless dan tidak menggunakan database atau menyimpan sesi.
Bisakah saya membatasi tool yang tersedia untuk agent?
Ya, Anda dapat menggunakan profile untuk memuat subset tool tertentu seperti finance, decision, atau simulation.
Apakah tersedia tier gratis?
Ya, tersedia tier gratis yang memungkinkan 20 panggilan per hari per IP tanpa memerlukan API key.