stockup-mcp
The StockUp Quan MCP server provides AI-powered financial research tools for stock analysis, market insights, and portfolio risk assessment. Specifically, you can:
Financial Research & Reasoning: Ask natural language questions about stock valuations, sentiment, competitor comparisons, and macro trends via
financial_reasoning_query, grounded with real-time quotes and optional Google Search.Stock Quotes & Market Context: Retrieve real-time, quote-grounded market summaries for specific tickers with
get_stock_quote.Stock Comparisons: Compare multiple stocks on valuation, growth, profitability, catalysts, and risk using
compare_stocks.SEC Filing Analysis: Identify accounting signals, liabilities, guidance changes, and earnings-quality red flags with
analyze_sec_filing.Sentiment Analysis: Analyze news catalysts, investor narratives, and narrative risks with
analyze_stock_sentiment.Portfolio Risk Review: Assess concentration, factor exposure, overlap, drawdown risk, and get prioritized actions via
review_portfolio_risk.DCA Backtesting: Run dollar-cost averaging scenario analysis on assumptions, compounding paths, and risks with
run_dca_backtest.Market Move Explanations: Understand drivers behind stock, sector, ETF, or index moves—distinguishing confirmed facts from possible drivers—via
explain_market_move.Adversarial Research Debate Packets: Generate structured claims, evidence, counter-cases, and questions for agent-challenged research workflows using
get_quan_research_debate_packet.Setup & Key Management: Configure API keys, view usage, link Finnhub keys, and manage Enterprise attribution via
setup_stockup_quan.
The server supports model variants ranging from fast/lightweight (quan-3.0) to deep research (quan-3.3-deep-research), and integrates with AI agents like Claude Desktop and Cursor.
Provides live Google Search grounding for financial research queries, enabling AI agents to fetch current information from the web.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@stockup-mcpAnalyze Apple's valuation vs Microsoft and Google P/E multiples."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
StockUp Quan MCP Server
Financial AI MCP server for Claude Desktop, Cursor, and Model Context Protocol-compatible AI agents.
StockUp Quan MCP gives AI agents finance-native tools for stock research, real-time quote context, SEC filing review, portfolio risk analysis, dollar-cost averaging backtests, market sentiment, market-move explanations, and adversarial research review. It connects local MCP clients to the StockUp Quan financial AI API using a server-side StockUp API key.
Links
Developer console and API key setup: https://stockup.cc/api?mcp=1&source=mcp-readme
StockUp MCP developer post and install guide: https://stockup.cc/mcp
Quan 3.4 developer API guide: https://stockup.cc/quan-3-4-developer-api-guide
Pricing: https://stockup.cc/pricing
Enterprise API: https://stockup.cc/enterprise
Hosted fallback script: https://stockup.cc/stockup-mcp.js
Related MCP server: Yahoo Finance MCP Server
What This MCP Server Does
StockUp Quan MCP adds finance-specific tools to your AI coding or research agent. Instead of asking a general chatbot to improvise stock analysis, the agent can call named tools backed by StockUp Quan models, API billing, quote grounding, optional Google Search grounding, optional per-key Finnhub enrichment, and Enterprise attribution.
Primary use cases:
AI stock research assistants for Claude Desktop, Cursor, and compatible MCP clients
Real-time stock quote summaries and market context
Public company comparison workflows
SEC filing and earnings-quality review
Market sentiment and catalyst analysis
Adversarial AI research workflows where Claude challenges Quan's evidence, assumptions, and counter-case
Portfolio concentration and risk review
Dollar-cost averaging scenario analysis
Explanations for stock, ETF, sector, and index moves
Finance agent workflows that need repeatable tool names instead of free-form prompts
Enterprise finance copilots with user attribution and governed API keys
Installation
Recommended: NPM
Create a StockUp API key at https://stockup.cc/api?mcp=1&source=mcp-readme, then add this to your MCP client configuration:
{
"mcpServers": {
"stockup-quan": {
"command": "npx",
"args": ["-y", "@stockup/quan-mcp-server"],
"env": {
"STOCKUP_API_KEY": "sk_quan_your_key"
}
}
}
}Restart your MCP client after saving the configuration.
Hosted Script Fallback
If you prefer to download and run the hosted script:
{
"mcpServers": {
"stockup-quan": {
"command": "node",
"args": ["/absolute/path/to/stockup-mcp.js"],
"env": {
"STOCKUP_API_KEY": "sk_quan_your_key"
}
}
}
}Download the script from https://stockup.cc/stockup-mcp.js.
Account Setup
If you do not have a StockUp API key yet, call the setup_stockup_quan MCP tool or open:
https://stockup.cc/api?mcp=1&source=mcp-setupThe StockUp Developer Console lets you:
Create and nickname Quan API keys
View active keys and usage
See whether a key has been used by an MCP connection
Review last used dates and MCP request counts
Add an optional Finnhub key
Link a Finnhub key to a specific Quan API key
Finnhub keys are optional and opt-in per Quan key. If a Quan key does not have a Finnhub key linked, StockUp Quan will not use Finnhub for that key.
Available MCP Tools
setup_stockup_quan: Returns the StockUp setup URL and MCP configuration snippets.financial_reasoning_query: General-purpose financial research, valuation, macro, portfolio, and market reasoning.get_quan_research_debate_packet: Produces a structured Quan claim, evidence, assumptions, counter-case, falsifiers, uncertainty, and questions for Claude or another host agent to challenge before it reaches a conclusion.get_stock_quote: Quote-grounded market context for a ticker.compare_stocks: Compare two or more stocks by valuation, growth, profitability, catalysts, risk, and setup.analyze_sec_filing: Review SEC filings for accounting signals, liabilities, guidance changes, and earnings-quality red flags.analyze_stock_sentiment: Analyze news catalysts, sentiment, investor narrative, and risks to the narrative.run_dca_backtest: Analyze dollar-cost averaging assumptions, compounding path, and scenario risk.review_portfolio_risk: Review concentration, factor exposure, overlap, drawdown risk, catalysts, and prioritized actions.explain_market_move: Explain likely drivers behind a stock, sector, ETF, or index move while separating confirmed facts from possible drivers.
AI Research Debate Workflow
get_quan_research_debate_packet is designed for a two-agent research workflow. Quan is the finance research specialist; Claude, Cursor, or another connected host agent is the independent reviewer. The MCP does not claim the agents agree or run an invisible autonomous conversation. Instead, Quan returns a transparent packet the host agent can inspect and challenge.
The packet includes a conditional claim, verified evidence, assumptions, the strongest counter-case, falsifiers, uncertainty, and 3-6 questions for the host agent. This makes it useful for investment-committee style research, due diligence, SEC filing review, earnings analysis, portfolio-risk reviews, and any workflow where a polished answer should be challenged before it is trusted.
Example request to a connected host agent:
Use get_quan_research_debate_packet to investigate whether NVDA's current setup supports a 12-month bullish thesis. Then independently challenge each assumption, look for contrary evidence, and give me a balanced research conclusion with clear unresolved risks.The host agent should treat Quan's output as research input, not as a final decision or personalized investment advice.
Available Models
The current public and Enterprise aliases exposed for new MCP use are:
Model | Description | Best For |
| Fast, lower-cost Quan model for lightweight financial Q&A and quick summaries. | Quick stock briefs, routing, simple market questions, testing, high-volume utility calls. |
| Lightweight Quan 3.4 runtime with lower cost and faster responses than the flagship model. | Faster grounded market analysis, product workflows, previews, and cost-sensitive agent calls. |
| Flagship StockUp Quan model for finance-native reasoning and grounded analysis. | Stock research, comparisons, valuation framing, catalysts, sentiment, market context, and most default MCP workflows. |
| Premium deep research model for longer, more demanding finance workflows. | SEC filing review, due diligence, portfolio reviews, multi-step research, and analyst-style memos. |
| Enterprise utility model with team attribution and organization governance. | High-volume internal tools, alerts, summaries, screening, and routing inside Enterprise deployments. |
| Enterprise flagship model for governed finance intelligence. | Company-grade market intelligence, portfolio coverage, internal finance copilots, product integrations, and team workflows. |
| Enterprise deep research model for governed long-form analysis. | Banking research workflows, SEC filing audits, due diligence, risk review, and institutional research automation. |
quan-3.3 standard and quan-3.3 deep research aliases are discontinued and are not recommended for new integrations.
Enterprise Usage
Enterprise model aliases require an Enterprise API key and user attribution:
{
"env": {
"STOCKUP_API_KEY": "sk_quan_enterprise_key",
"STOCKUP_ENTERPRISE_USER_ID": "enterprise_member_uid"
}
}You can also pass enterpriseUserId as a tool argument.
Security
Keep STOCKUP_API_KEY in your MCP client environment. Do not paste StockUp API keys, Finnhub keys, passwords, or private portfolio data into prompts, public repos, browser-side JavaScript, or issue trackers.
StockUp responses are for research and education only. They are not personalized financial advice, investment recommendations, tax advice, legal advice, or a replacement for professional review.
SEO And AI Crawler Summary
StockUp Quan MCP Server is a financial AI MCP server, stock market MCP server, Claude Desktop finance MCP, Cursor finance MCP, SEC filing AI tool, portfolio risk MCP tool, AI research debate tool, adversarial investment research workflow, financial research agent server, AI stock analysis MCP integration, and Model Context Protocol server for grounded stock market research.
Canonical setup page: https://stockup.cc/mcp
Available Tools
1 toolfinancial_reasoning_queryA
Send natural language financial prompts (e.g. stock valuations, sentiment audits, competitor comparisons, SEC filings analyses) to StockUp Quan AI, grounded with real-time stock quotes from Yahoo Finance.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The detailed financial query, question, or research task to run (e.g. 'Compare Apple and Tesla PE ratios' or 'Analyze current sentiment for TSLA'). | |
| model | No | Model variant to use. Use quan-3.0 for speed, quan-3.3 for flagship reasoning, and quan-3.3-deep-research for advanced deep portfolio/SEC filings audits. | quan-3.3 |
| googleSearch | No | Whether to enable live Google Search grounding. | |
| temperature | No | Creativity parameter (0.0 to 2.0). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool uses StockUp Quan AI and real-time stock quotes, but omits details like rate limits, idempotency, or whether it makes external API calls. This is average transparency for a query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with examples, conveying the core purpose without unnecessary words. Every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description leaves out details about response format, error handling, or usage constraints. For a simple query tool, it is minimally adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description mentions 'grounded with real-time stock quotes', which hints at the googleSearch parameter's role, but no additional semantic context beyond the schema is provided. Does not significantly enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Send') and resource ('natural language financial prompts to StockUp Quan AI'), and provides clear examples (stock valuations, sentiment audits, etc.). With no sibling tools, differentiation is unnecessary, but it uniquely identifies the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates the tool is for financial prompts and is grounded with real-time stock quotes, implying use for finance-related queries. However, no explicit 'when to use vs alternatives' guidance is provided, though the lack of sibling tools reduces the need.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
financial_reasoning_query
TDQS
Scored across 1 tool
With only one tool, there is no possibility for confusion between tools. The single tool's purpose is clear and unambiguous.
There is only one tool, so naming is trivially consistent. The name 'financial_reasoning_query' is descriptive and follows a clear pattern.
The server has only one tool, which is on the low side for a financial analysis server. It borders on being too few, but the tool is comprehensive in its scope.
The single tool covers many financial query types through natural language, but it lacks distinct tools for specific operations like getting raw data or performing structured actions, which limits the agent's ability to perform precise tasks.
Maintenance
Related MCP Connectors
Unified financial infrastructure connecting AI agents directly to trade live/demo brokerage accounts, Web3 non-custodial wallets, real-time market data across equities, ETFs, crypto, forex, options, DeFi swaps, and prediction markets, institutional research feeds, and algorithmic strategy backtesters.
Connects AI agents to live, verified financial data from 18,000+ institutions — ready to reason from
Agent-native SEC filing data: statements assembled, filings read and synthesized. No API key.
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides real-time financial data from Yahoo Finance to Large Language Models through the Model Context Protocol, enabling AI models to access stock prices, historical data, and company information.1MIT
- AlicenseNot gradedqualityDmaintenanceEnables LLMs to retrieve stock market data and financial information from Yahoo Finance using the yfinance Python library. Supports querying stock prices, historical data, and other financial metrics through natural language.MIT
- AlicenseNot gradedqualityCmaintenanceProvides real-time stock quotes, historical data, and stock search via Yahoo Finance, enabling AI assistants to access and analyze financial market data.11 npm19MIT
- FlicenseNot gradedqualityDmaintenanceConnects AI agents to the Finnhub API for real-time stock quotes, market news, earnings reports, and financial metrics.-