Stock Analyzer Mcp

Stock Analyzer MCP

> 📡 The Model Context Protocol server bundled with Stock Analyzer — a macOS desktop app for Taiwan + US stock market analysis. > > An MCP server with deep Taiwan stock coverage (TWSE / TPEx + three major institutional flows + chip data + monthly revenue). 95 tools across 15 categories + 6 resources. Local-first — runs in-process inside the Electron app, no API costs, no cloud dependency.

Current version: MCP server 1.3.0 · Stock Analyzer app 0.48.0-beta · Updated 2026-06-30

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⚠️ How this MCP server actually works

This repo contains the MCP shim source (mcp-server.js + lib/ai-tools + Dockerfile). The shim is a thin HTTP-to-stdio bridge — when an MCP client invokes a tool, the shim proxies the call to http://localhost:3000/api/*, where the Stock Analyzer desktop app's embedded Express backend does the actual work (DB query, computation, analysis).

> The MCP server in this repo, run standalone (e.g. via docker run), can advertise its 95 tools through introspection but cannot execute them. You need Stock Analyzer running on the same machine for tools to actually return data.

This split is intentional — the analysis engine + market data + license-gated features live in the closed-source desktop app; the MCP shim is open-source (MIT) so the integration surface is fully transparent.

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Why this repo exists

The Stock Analyzer desktop app itself is a commercial product (Lite tier free, Standard NT$1,499, Premium NT$2,999 — all one-time purchases, no subscription). This repo exists to:

• Open-source the MCP shim layer under MIT so marketplaces (awesome-mcp-servers, mcpservers.org, PulseMCP, Glama) can build & verify a working image
• Provide a public canonical link for MCP discovery
• Host the integration guide separately from the closed app source
• Make Claude Desktop / Claude Code / agentic frameworks easy to configure against the bundled MCP server

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Build (Docker, for Glama / marketplaces)

docker build -t stock-analyzer-mcp .
docker run -i --rm stock-analyzer-mcp   # stdio JSON-RPC on stdin/stdout

Image is ~258 MB (node:20-alpine + 2 npm deps). The build skips better-sqlite3, Electron, and other backend-only dependencies because the shim itself never imports them — all data calls go via HTTP to the locally-running Stock Analyzer app's /api/* endpoints.

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What's in this MCP server

95 tools across 15 categories

| Category | Tools | Examples | |---|---|---| | market (14) | Quotes, history, heatmap, sector ranking, news, FX, seasonality, ETF holdings, trading-day status | get_stock_price, get_market_heatmap, get_seasonality | | chips (6) | Three major institutional flows, fund flow Sankey, insider alerts, abnormal blocks, margin ranking | get_institutional_flow, get_fund_flow_sankey | | fundamentals (6) | Financial statements, monthly revenue, dividends, EPS, DCF valuation | get_financial_statements, calculate_dcf | | technical (5) | RSI / MACD / KD / Bollinger / Beta / correlation / candlestick patterns | get_technical_indicators, detect_kline_patterns | | macro (8) | FED policy, yield curve, inflation, employment, earnings calendar | get_macro_snapshot, get_fed_policy_stance | | sentiment (6) | News sentiment, market sentiment, per-stock sentiment, forecasts, entry strategies, TAIFEX put/call ratio | get_stock_sentiment_v2, get_sentiment_forecasts | | portfolio (11) | Holdings, P&L, performance, concentration, signals, trade CRUD | get_portfolio, get_portfolio_concentration | | backtest (5) | Single-stock, multi-strategy, grid search, MC factor mining, random portfolio | backtest_strategy, monte_carlo_factor_mining | | risk (6) | VaR, systemic risk, portfolio optimization, marginal/component VaR contribution, stress test, scenario stress propagation | get_systemic_risk, get_risk_contribution, run_scenario | | ai workflow (7) | Full-stock analysis, screener, workflows, notes, + deep-dive debate + daily briefing + candidate comparison + post-trade review | research_stock_deep_dive, portfolio_daily_briefing | | thesis (7) | Investment hypothesis CRUD + quality evaluation | upsert_thesis, evaluate_thesis_quality | | watchlist (4) | Watchlist CRUD | add_watchlist | | alert (3) | Price alerts | set_price_alert | | backfill (2) | Admin data backfill | trigger_backfill | | forecast (5) | Price probability cone (GBM Monte Carlo), un-gameable forecast-calibration track-record, TW pre-open cross-market context, as-of knowability replay (multi-method calibration), per-stock pre-open US lead | get_price_forecast, get_asof_replay, get_stock_preopen_lead |

Every tool carries:

• annotations.readOnlyHint — whether the tool modifies state (clients auto-confirm before destructive ops)
• annotations.destructiveHint — delete_* / cancel_* flagged true
• annotations.idempotentHint — upsert_* / update_* flagged true
• _meta.tw.stockanalyzer/estimated_cost_usd — worst-case LLM cost (most tools $0; deep-dive ~$0.16)

6 resources (Claude Desktop @-mentionable)

Inject context into your conversation without burning tool calls:

| Resource | Content | |---|---| | saa://portfolio | Full holdings (TW + US, USD/TWD unified pricing, unrealized P&L) | | saa://watchlist | All watchlist entries with live quotes + alert states | | saa://thesis | Active investment theses (hypothesis, key levels, next review dates) | | saa://market/today | Three major institutional flows / sector winners / systemic risk / FX | | saa://reports/recent | Latest portfolio briefing (free; doesn't auto-trigger LLM) | | saa://system/info | Server introspection (version, schema version, active profile, tool count) |

Profiles (filter what gets exposed)

Set SAA_MCP_PROFILE env var to gate which tools are visible to the LLM client:

| Profile | Tools exposed | Use case | |---|---|---| | default (omit) | All 95 | Your personal Claude Desktop | | safe_readonly | 80 read-only tools | Shared / untrusted LLM clients — blocks add_trade / delete_* / upsert_thesis / set_price_alert / etc. |

Resources stay available in both profiles (they're read-only by definition).

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How it compares

| Server | TW coverage | US coverage | Local | License model | |---|---|---|---|---| | Alpha Vantage MCP | ⚠️ Delayed quotes only | ✅ Full | ❌ Cloud API | Pay per call | | Financial Datasets MCP | ❌ None | ✅ Full | ❌ Cloud API | Subscription | | EODHD MCP | ⚠️ EOD only | ✅ Full | ❌ Cloud API | Subscription | | Lambda Finance | ❌ None | ✅ Full + options | ❌ Cloud | Subscription | | Stockflow (Yahoo) | ⚠️ Spotty TW data | ✅ Full | ❌ Cloud | Free (rate-limited) | | Stock Analyzer MCP | ✅ Deep TWSE + TPEx + institutional + chip | ✅ Full | ✅ Local SQLite | One-time license (Lite free) |

For non-Taiwan readers: Taiwan stock market has its own data ecosystem (TWSE, TPEx OpenAPI, three major institutional investors, monthly revenue reporting) that's nearly absent from English-speaking financial data platforms. If you want an AI agent that can answer "How are TSMC's institutional investors trading lately?" or "Find me TW small-caps with >30% YoY revenue growth", Stock Analyzer MCP is built for exactly this — deterministic TW chip/institutional/revenue tools that English-focused MCP servers generally lack.

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Quickstart: Claude Desktop

1. Install Stock Analyzer

Get the free Lite tier from stockanalyzer.tw. Version 0.47.4-beta or later ships MCP server v1.2.0.

2. Configure Claude Desktop

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

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/Applications/Stock Analyzer.app/Contents/Resources/app.asar.unpacked/bin/saa-mcp",
      "env": { "PORT": "3000" }
    }
  }
}

> Why the wrapper? Running node mcp-server.js directly hits a better-sqlite3 ABI mismatch (the binding is compiled for Electron's Node, not the system's). The bin/saa-mcp wrapper auto-finds the SAA Electron runtime and runs the MCP server with ELECTRON_RUN_AS_NODE=1. Older configs that point to node will need updating.

3. (Optional) Restrict to read-only mode

If the LLM client isn't fully trusted (shared Claude project, third-party agent), add:

"env": { "PORT": "3000", "SAA_MCP_PROFILE": "safe_readonly" }

This blocks 15 write tools (add_trade, delete_trade, upsert_thesis, set_price_alert, etc.) but keeps all read tools + all 6 resources.

4. Fully restart Claude Desktop (cmd+Q then reopen)

5. Try it

> "List all SAA stock-analyzer tools" > > "Analyze 2330 — institutional flow last month + 3-month momentum + radar score + give me a buy/sell view" > > "@saa://portfolio — what's my biggest concentration risk?" > > "Compare 2330, 2454, and 3008 as candidates. Include their theses if they exist."

Claude will orchestrate multiple tool calls (or @-mentions for resources) and synthesize a research report.

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Headline tools (2026-05-18)

🎭 research_stock_deep_dive — Premium tier

5 specialized AI agents debate in parallel:

• 🐂 Bull (only sees evidence supporting an upside thesis)
• 🐻 Bear (only sees evidence supporting a downside thesis)
• 📰 Sentiment (news + social signals)
• 🛡️ Risk (volatility, drawdown history, regime context)
• 🎯 Synthesizer (sees all four; produces a 6-level action: strong_buy → avoid)

Each agent uses a distinct subset of the 95 tools. Output includes per-agent reasoning + final action + confidence score. ~$0.16/call LLM cost (Anthropic Sonnet / OpenAI).

🌅 portfolio_daily_briefing — Lite tier

Pre-market or post-market portfolio briefing. Aggregates current holdings, unrealized P&L, sector exposure, relevant macro / institutional flow into an actionable summary.

• mode='get' → reads the latest cached briefing (free, instant)
• mode='generate' → runs a fresh one (~10-20s, ~$0.04/call LLM cost)

🔍 compare_investment_candidates — Lite, cost $0

Side-by-side deep analysis of 2-5 candidate stocks. Parallel fan-out of get_full_stock_analysis (fundamentals + technical + chip + institutional + levels) per candidate, plus existing thesis status. Deterministic — the agent sees raw evidence rather than an LLM-synthesized opinion, which empirically produces better reasoning.

📓 post_trade_review — Lite, cost $0

Past-N-days reflection. Aggregates analyze_trade_performance (FIFO P&L, win rate, hold time) + get_trade_journal (recent trades) + get_portfolio_signals (current state). Auto-detects observable patterns:

• low_win_rate ( avg win) → poor stop-loss discipline

Hands the agent objective indicators to write narrative review against.

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Documentation

• Full MCP usage guide (zh-TW + en): [MCP-USAGE-GUIDE.md](./MCP-USAGE-GUIDE.md) — Claude Desktop setup, troubleshooting, conversation examples
• Launch blog post (bilingual): [docs/mcp-launch-2026-05.md](./docs/mcp-launch-2026-05.md) — context on the 2026 MCP finance landscape + why TW coverage was the gap
• Tool reference: bundled inside the app at Settings → 🔌 MCP / Agent

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Design philosophy

• Local-first: All data lives in ~/.twse-analyzer/stock_history.db (SQLite, single file). MCP server runs in-process inside the Electron app via stdio transport.
• BYOK LLM: SAA itself has an AI Hub that consumes the same 95 tools. Bring your own keys (Claude / GPT / Gemini / Ollama). The MCP server itself isn't tied to any LLM — it just exposes deterministic data + a few LLM-backed aggregators.
• Transparent methodology: 16 bilingual methodology pages (zh-TW + en) explain every analytical tool's formula, data source, and limitations. Available at /methodology.html inside the app.
• No active trading signals: Research output only — not order execution. Regulatory + product positioning decision.
• Cost honesty: Every tool surfaces its worst-case LLM cost upfront via _meta.tw.stockanalyzer/estimated_cost_usd. No hidden cloud-API spend.

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Versioning

The MCP server uses two version numbers:

| Field | Meaning | Bump on | |---|---|---| | server_version | SAA MCP binary version (shown at initialize) | Each SAA app release | | tools_schema_version (in saa://system/info) | Tool/resource shape version | Tool added/removed/renamed/required-changed |

Rules:

• patch — additive (new tool, new resource)
• minor — new required param, new enum restriction, readOnlyHint change
• major — rename, removal, required-keys change

Current: server 1.2.0, schema 1.2.0. Changelog inside mcp-server.js header.

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License

This documentation repo is MIT licensed (see [LICENSE](./LICENSE)). The Stock Analyzer app itself is closed-source commercial software.

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Contact

• Website: stockanalyzer.tw
• Email: hello@stockanalyzer.tw
• Issues: Use GitHub Issues on this repo for MCP integration questions
• For app feature requests or bug reports: email above

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kevinlin49361128-stack
• Source: kevinlin49361128-stack/stock-analyzer-mcp
• License: MIT

Install and usage instructions live in the source repository linked above.