MCP.Market

Quant Research MCP server

by FlawlessByte·io.github.FlawlessByte/quant-research-mcp·v0.1.0
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Paper-backed quant trading methods and a deterministic, no-LLM decision helper for serious traders.

B82/100grade B
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Safety scan
B82/100

full report

Adoption
New

0 stars21 downloads/wk

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Quant Research MCP tools (13, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • quant_analyze_setupwrite action

    Run a registered method on a ticker to produce a trade setup.

  • quant_analyze_universewrite action

    Run a universe-based method (ranking or pairs) across multiple tickers.

  • quant_backtest_method

    Backtest a registered method by replaying its own analyze() over history.

  • quant_check_events

    Upcoming earnings and dividend events for a ticker.

  • quant_compute_indicators

    Compute EMA9/EMA20, RSI, ATR, VWAP and the Hurst exponent for a ticker.

  • quant_describe_method

    Full detail and citation for one registered method.

  • quant_detect_regime

    Classify a ticker's regime from its daily Hurst exponent.

  • quant_fetch_news

    Fetch recent headlines for a ticker. Data only — no sentiment is computed.

  • quant_list_methods

    List every registered paper-backed trading method.

  • quant_market_context

    Index/VIX/sector tape read for the current session.

  • quant_portfolio_risk

    Stateless portfolio heat / correlation / concentration check.

  • quant_score_decision

    Deterministically score a setup and decide ENTRY / NO_ENTRY.

  • quant_screen_universe

    Rank tickers by intraday tradeability (gap, relative volume, ATR%).

Public scan report

scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan26 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 106 days ago8/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 82/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Quant Research MCP compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
82/100
higher than 65% of 4,460 AI and agents servers
Tools
13
the typical AI and agents server has 6
Tools that change things
15%
typical: 0%. Fewer write tools means less an agent can break
Downloads a week
21
more than 7% of 1,538 AI and agents servers with a package

What its tools let an agent do: 11 read · 2 change

Install Quant Research MCP in Claude Code, Cursor or VS Code

claude mcp add quant-research-mcp -- uvx quant-research-mcp
Add to Cursor

What the publisher says

From the Quant Research MCP repository's README, as published. We do not edit it. Read it on GitHub

quantresearchmcp

<!-- mcp-name: io.github.FlawlessByte/quant-research-mcp -->

An MCP server exposing a registry of paper-backed quantitative trading methods plus a deterministic, no-LLM decision helper. The server supplies reproducible math (screening, indicators, regime detection, method signals, scored entry decisions); the calling agent supplies judgement (e.g. reading headlines into a sentiment signal). No claude -p, no subprocess, no LLM inside the server.

⚠️ Not financial advice. This is read-only **research and educational

tooling. It performs quantitative computation only and places no orders**.

Nothing it outputs is investment advice or a recommendation. Market data may be

delayed or wrong, backtests are not forecasts, and trading carries substantial

risk of loss. You alone are responsible for any capital you risk. Provided

"as is" without warranty — see LICENSE.

Why this exists

Repackages a working day-trading pipeline as composable MCP tools, with two goals:

a registry. Adding a new paper = drop one module, call register(...), import it. Nothing else changes.

  1. Extensible to future papers. Each strategy is a TradingMethod registered in

with an LLM call. Here that is replaced by quantscoredecision — a pure function combining reward:risk, regime strength, volume, RSI positioning and ATR-normalised stop quality into an auditable score. Same inputs → same output.

  1. Decision-making is scientific, not generative. The old pipeline gated entries

Tools (13)

All tools are read-only and support response_format: markdown | json.

Workflows

Day trade

quant_market_context                  # tape read: risk-on/off, sectors
  → quant_screen_universe             # find movers
  → quant_analyze_setup               # hurst_regime_orb on the top name
  → quant_check_events + quant_fetch_news   # binary risk + agent sentiment
  → quant_portfolio_risk              # heat/correlation vs your open book
  → quant_score_decision(setup, news_sentiment, days_to_earnings,
                         portfolio_heat_pct)

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Quant Research MCP: common questions

Is Quant Research MCP server safe?
Mostly: it is graded B (82/100). Read the Quant Research MCP safety report
How do I install Quant Research MCP?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Quant Research MCP need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Quant Research MCP maintained?
The last commit was 144 days ago (2026-06-13). The latest release is v0.1.0.
What can I use instead of Quant Research MCP?
Servers from other publishers that do the same job: CHAP Coordinator MCP server, Gemini MCP server and three.ws Portfolio MCP server.
How does Quant Research MCP compare with other AI and agents MCP servers?
safety score 82/100 (higher than 65% of 4,460 AI and agents servers); tools 13 (the typical AI and agents server has 6); tools that change things 15% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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