StoQuant — Quantitative Stock Research

Name: StoQuant — Quantitative Stock Research
Author: ohadf2015

by ohadf2015version 0.6.0i★ –iRuns on your computer

Registry nameio.github.ohadf2015/stoquant-mcp

Install

Overview

Description from the maker (original English)

StoQuant: 61 tools — Q-Score, ML, screeners, alt-data, portfolio, backtest, strategy.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: STOQUANT_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: STOQUANT_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "stoquant": {
      "command": "npx",
      "args": [
        "-y",
        "stoquant-mcp"
      ],
      "env": {
        "STOQUANT_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add stoquant --env STOQUANT_API_KEY=your-value -- npx -y stoquant-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "stoquant": {
      "command": "npx",
      "args": [
        "-y",
        "stoquant-mcp"
      ],
      "env": {
        "STOQUANT_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "stoquant": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "stoquant-mcp"
      ],
      "env": {
        "STOQUANT_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

STOQUANT_API_KEY secret required env
StoQuant API key. Mint one free (100 requests/day) at https://stoquant.com/account/api-keys

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account ohadf2015, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5668 servers in this category
Published to the registry more recently than 66% of the other 5668 servers in this category
Basic checks equal to or better than 100% of the other 5668 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is StoQuant — Quantitative Stock Research safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: STOQUANT_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.6.0) reached the registry 20 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

stoquant-mcp

Model Context Protocol (MCP) server for StoQuant — gives Claude (and any MCP-compatible agent) direct, read-only access to StoQuant's quant signals, ML predictions, screening, ownership, and macro data.

Free to start. A free StoQuant API key runs the core tools (quotes, Q-Score, screeners, market regime) at 100 requests/day. Power removes the cap and unlocks the premium tools (ML, alt-data, insider clustering). Get a key at https://stoquant.com/account/api-keys.

Source & listings: GitHub · npm · Official MCP Registry (io.github.ohadf2015/stoquant-mcp) · Landing page

One-command install

npx -y stoquant-mcp install

You'll be prompted for your API key. The installer writes the right claude_desktop_config.json for your OS, backs up any existing config, then prints the next step.

After install, fully quit and relaunch Claude Desktop. Type list available tools to confirm the stoquant_* tools are loaded.

Manual install (alternative)

Add this to your claude_desktop_config.json:

"mcpServers": { "stoquant": { "command": "npx", "args": ["-y", "stoquant-mcp", "serve"], "env": { "STOQUANT_API_KEY": "sk_live_…" } } } }

Config locations:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

Tools (61)

Category	Tools
Prices	`get_quote`, `get_quotes_batch`, `get_extended_quote`, `get_sparklines`, `get_benchmarks`, `get_sector_performers`
Research	`get_qscore`, `get_signals_v2`, `get_ml_prediction`, `explain_ml_prediction`, `get_strategic_edges`, `get_quant_analytics`, `get_evidence`
Signals	`get_market_regime`, `get_signal_accuracy`, `get_accuracy_summary`
Discovery	`get_top_conviction`, `get_hidden_gems`, `get_multibagger_candidates`, `get_related_stocks`, `get_signal_accuracy_heatmap`
Screener	`list_prebuilt_screens`, `run_prebuilt_screen`, `run_screener`
Ownership	`get_insider_trades`, `get_insider_cluster`, `get_big_insider_buys`, `get_institutional_holders`, `get_institutional_ratings`, `get_analyst_estimates`, `get_analyst_price_targets`, `get_analyst_changes`, `get_short_interest`
Filings & News	`get_sec_filings`, `get_news`, `get_news_events`
Social	`get_social_sentiment`, `get_social_trend`, `get_trending_social`, `get_social_momentum`, `get_source_credibility`
Catalysts	`get_catalysts`, `get_catalyst_watch`
Alt-data	`get_alt_signals` (FDA, CPSC, SEC suite, clinical trials, GitHub, App Store, Hacker News, news tone — one call), `get_alt_data` (Google Trends, Wikipedia views, options IV)
Options flow	`get_unusual_options`
Portfolio	`get_auto_portfolio_status`, `get_auto_portfolio_history`, `get_portfolio_health`, `optimize_portfolio`
Backtest	`list_backtest_templates`, `run_backtest`, `get_backtest`, `get_backtest_history`
Strategy	`get_strategy_performance`, `list_strategy_presets`, `get_strategy_preset_candidates`
Macro	`get_macro_indicator`, `get_macro_dashboard`, `get_macro_calendar`, `get_macro_themes`

Most tools are read-only fetches. run_backtest persists a simulation run (no live or paper orders). There is no order placement, cash move, or holdings mutation. Each tool is annotated readOnlyHint so agents know it is safe to call.

Accuracy and the no-edge gate

get_accuracy_summary hits the public /public/accuracy/summary scorecard. Read excessSpreadPp (alpha spread) and dataSufficient — do not treat `hitRate` as skill. When dataSufficient && excessSpreadPp < 0, auto-portfolio buys are suppressed (gated:no_edge). Call this before any buy-oriented portfolio read.

Portfolio/backtest/strategy tools are evaluate/read/backtest/optimize. This server never places orders. POST /auto-portfolio/evaluate executes paper trades and is not exposed.

Two things to know

The Q-Score (get_qscore) is the headline 0-100 composite, computed on *sector-residualized* returns (sector beta removed). Start here for "is this a good stock".
The ML prediction (get_ml_prediction) returns the *probability of outperforming the S&P 500* over a horizon (classification: bull|bear) — it is not a buy/hold/sell rating.

Resources

The server exposes reference docs an agent can read on demand:

stoquant://glossary — what Q-Score, ML outperformance, regimes, and signal accuracy mean, plus unit conventions.
stoquant://methodology — sector residualization, IC-weighting, ML health gating, and screener field semantics.

Prompts

Pre-built workflows that chain the tools:

analyze_ticker(ticker) — full single-stock deep dive (Q-Score + ML + ownership + edges → verdict).
find_opportunities(style) — generate and vet a shortlist for a style (value, momentum, growth, hidden gems).
market_briefing — top-down snapshot (regime + macro + benchmarks + conviction).

Using the screener

run_screener filters on exact camelCase field names. An unknown field is treated as null server-side and silently excludes every stock, so use the documented names:

trailingPE, priceToBook, pegRatio, marketCap, debtToEquity, currentRatio, grossMargins, operatingMargins, revenueGrowth, earningsGrowth, rsi14, fiftyDayAverageChangePercent, twoHundredDayAverageChangePercent, fiftyTwoWeekHighChangePercent, shortPercentOfFloat, numberOfAnalystOpinions, sector, regularMarketPrice, regularMarketVolume, regularMarketChangePercent.

Margins/growth are decimals (0.25 = 25%); marketCap is raw USD (250000000 = 250M). Operators: gt, gte, lt, lte, eq, between (value: [low, high]), in (value: [string, …]).

The screener reads from StoQuant's cached daily fundamentals (fast, refreshed daily). Prefer `run_prebuilt_screen` (pre-tuned) or run_screener with universe: "sp500" and a few filters.

Example prompts

"Get the Q-Score for NVDA and explain the top features driving the ML call."
"Show me the top 10 conviction stocks and their current ML outperformance odds."
"What's the current market regime, and which sectors are leading?"
"Run the undervalued-gems prebuilt screen on the sp500 universe."
"Screen sp500 for trailingPE under 15 and debtToEquity under 0.5."
"Get insider activity for PLTR over the last quarter."
"What is the live accuracy grade and excessSpreadPp? Do not buy if the tail is inverted."
"Show auto-portfolio last eval and holdings. If gated:no_edge, do not recommend buys."
"List backtest templates and run momentum on sp500 for 2023."

Security

Server requires STOQUANT_API_KEY — fails fast on boot if missing or malformed.
All requests use HTTPS. Bearer token in Authorization header. Never logged.
Tickers are validated (^[A-Za-z0-9.\-^]{1,10}$) before any external use.
Client-side rate limit: 200 req/min (configurable via STOQUANT_RATE_LIMIT_RPM).
30s per-request timeout (configurable via STOQUANT_TIMEOUT_MS).
Server-side: API key is hashed (SHA-256) at rest, prefix-indexed for fast lookup, timing-safe-compared on verify.
Revoke any key from https://stoquant.com/account/api-keys at any time.

Environment variables

Var	Default	Description
`STOQUANT_API_KEY`	— (required)	Your StoQuant API key (free or Power tier)
`STOQUANT_BASE_URL`	`https://stoquant.com/api`	API base URL
`STOQUANT_RATE_LIMIT_RPM`	`200`	Client-side rate limit (req/min)
`STOQUANT_TIMEOUT_MS`	`30000`	Per-request timeout
`STOQUANT_DEV`	`0`	Set `1` to allow `http://` base URLs (local dev only)

License

MIT

StoQuant — Quantitative Stock Research

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

stoquant-mcp

One-command install

Manual install (alternative)

Tools (61)

Accuracy and the no-edge gate

Two things to know

Resources

Prompts

Using the screener

Example prompts

Security

Environment variables

License