Click on "Install 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., "@QuantContext Screen S&P 500 for value stocks with PE under 15 and ROE above 12%"
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.
QuantContext is an MCP server that turns plain-English strategy descriptions into executable quant research: screen stocks by any criteria, backtest over historical data, and run factor analysis to see where the returns come from. Every number is computed from real market data, not generated by an LLM. Results are fully reproducible.
Works with Claude, Codex, OpenCode, or any other MCP-compatible coding agent.
pip install quantcontext-mcpClaude Code:
claude mcp add quantcontext -- quantcontextClaude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"quantcontext": {
"command": "quantcontext"
}
}
}No API keys. No configuration.
Related MCP server: panther-mcp
Three tools that compose into a full research workflow:
screen_stocks -> backtest_strategy -> factor_analysisTool | What it does |
| Filter S&P 500, Nasdaq 100, or Russell 2000 by fundamentals, momentum, quality, technical signals, or a multi-factor blend. Returns ranked candidates. |
| Test a strategy over history with a rebalance-loop engine. Returns CAGR, Sharpe, max drawdown, equity curve, and trade log. |
| Decompose strategy returns into Fama-French factors (market, size, value, momentum). Returns alpha with t-statistic, factor loadings, and R-squared. |
Stock screening:
Screen S&P 500 for value stocks: PE under 15, ROE above 12%Find the top 20% momentum stocks in the Nasdaq 100 over the last 200 daysRank S&P 500 stocks by a blend of value, momentum, and quality, equal weight each factorFind S&P 500 stocks with RSI under 40 and price above the 200-day moving averageBacktesting:
Backtest a top-20% momentum strategy on Nasdaq 100, monthly rebalance, last 2 yearsHow would a value screen (PE under 15, ROE above 12%) have performed on S&P 500 over the last 3 years?Test a momentum strategy with a 15% stop loss and 20% max portfolio drawdown circuit breakerFull research workflow:
Screen S&P 500 for cheap, high-quality stocks. Backtest monthly over 3 years,
then run factor analysis. Is the return real alpha or just factor exposure?Screen | Description | Key parameters |
| Filter by PE, ROE, leverage, revenue growth |
|
| Profitability and balance sheet health |
|
| Rank by N-day price momentum |
|
| Cheapest stocks by valuation |
|
| Multi-factor composite score |
|
| RSI and SMA crossover signals |
|
| Stocks below z-score threshold |
|
The tools are also importable directly — no agent required. Useful if you have an existing script and want to plug in backtesting or factor analysis.
from quantcontext.server import screen_stocks, backtest_strategy, factor_analysis
import asyncio, json
# Screen
result = json.loads(asyncio.run(screen_stocks(
universe="sp500",
screen_type="fundamental_screen",
config={"pe_lt": 15, "roe_gt": 12},
)))
# Backtest
bt = json.loads(asyncio.run(backtest_strategy(
stages=[{"order": 1, "type": "screen", "skill": "fundamental_screen", "config": {"pe_lt": 15, "roe_gt": 12}}],
universe="sp500",
rebalance="monthly",
start_date="2022-01-01",
)))
print(bt["metrics"])
# Factor analysis — pipe the equity curve straight in
fa = json.loads(asyncio.run(factor_analysis(
equity_curve=bt["full_equity_curve"]
)))
print(fa["alpha_annualized"], fa["alpha_tstat"])Strategies are expressed using the built-in screen types from the table above. All functions are async and return JSON strings.
All public data, no API keys required.
Data | Source | Cache |
Daily OHLCV prices | Yahoo Finance ( |
|
Fundamentals (PE, ROE, margins, etc.) | Yahoo Finance |
|
Fama-French factors (Mkt-RF, SMB, HML, Mom) | Kenneth French Data Library |
|
Universe lists (S&P 500, Nasdaq 100) | Wikipedia |
|
The first tool call downloads and caches data (10-30 seconds). All subsequent calls use the local cache: screening under 1s, backtesting 3-8s.
To skip the cold start, run once after install:
quantcontext-warmup --url https://quantcontext.ai/api/dataMIT
Run a historical backtest on a stock screening strategy. Uses a rebalance-loop engine that re-runs the screening pipeline on each rebalance date, sizes positions, enforces risk limits, and tracks daily P&L.
Returns equity curve, trade log, and performance metrics including CAGR, Sharpe ratio, maximum drawdown, Calmar ratio, win rate, and turnover.
The backtest is fully deterministic — same inputs always produce identical results.
After backtesting, use factor_analysis on the equity_curve to decompose returns into Fama-French factors (market, size, value, momentum) and estimate true alpha.
| Name | Required | Description | Default |
|---|---|---|---|
| stages | Yes | Pipeline stages defining the strategy. Each stage is an object with: order (int), type ('screen'|'analyze'|'signal'), skill (skill name), config (dict). Example: [{order: 1, type: 'screen', skill: 'fundamental_screen', config: {pe_lt: 15}}, {order: 2, type: 'signal', skill: 'momentum_screen', config: {lookback_days: 200, top_pct: 0.3}}] | |
| universe | No | Stock universe. Options: sp500, russell2000, nasdaq100 | sp500 |
| rebalance | No | Rebalance frequency. Options: daily, weekly, monthly, quarterly | monthly |
| sizing | No | Position sizing method. Options: equal_weight, inverse_volatility | equal_weight |
| start_date | No | Backtest start date in YYYY-MM-DD format | 2023-01-01 |
| end_date | No | Backtest end date in YYYY-MM-DD format. Defaults to today. | |
| max_position_size | No | Maximum weight per position (0-1). E.g., 0.1 = 10% max per stock | |
| stop_loss | No | Per-position stop loss (0-1). E.g., 0.15 = sell if position drops 15% | |
| max_drawdown | No | Maximum portfolio drawdown before going to cash (0-1). E.g., 0.2 = 20% |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors beyond annotations: fully deterministic, rebalance-loop engine, risk enforcement, and output details (equity curve, trade log, performance metrics). No contradiction with annotations.
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?
Succinct and well-structured, with the main purpose front-loaded. Includes all necessary information without redundancy. Uses clear breaks for outputs and usage guidance.
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 the existence of an output schema, the description sufficiently covers return values and behavioral context. It explains the engine, determinism, and provides post-backtest guidance, making it complete for agent use.
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 the schema already documents all parameters adequately. The description adds no extra parameter-level information, meeting the baseline expectation.
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 clearly states the tool runs a historical backtest on a stock screening strategy, specifying the engine type, outputs, and determinism. It distinguishes from siblings like screen_stocks (screening) and factor_analysis (post-backtest decomposition).
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?
Provides guidance on when to use this tool (for backtesting) and a clear next step to use factor_analysis. However, it doesn't explicitly state when not to use it or mention alternatives for live trading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decompose strategy or portfolio returns into Fama-French factors using OLS regression.
Breaks down returns into exposures to four systematic factors:
Mkt-RF (market risk premium): how much return comes from overall market movement
SMB (small minus big): size factor exposure
HML (high minus low): value factor exposure
Mom (momentum): momentum factor exposure
Also estimates alpha (excess return not explained by factors) with t-statistic for statistical significance. A |t-stat| > 2 suggests statistically significant alpha.
Returns alpha (daily and annualized), factor loadings with t-statistics, R-squared (how much of return variance is explained by factors), and residual volatility.
Use this after backtest_strategy to understand WHERE your returns come from — is it genuine alpha or just factor exposure?
| Name | Required | Description | Default |
|---|---|---|---|
| equity_curve | Yes | Equity curve as a list of {date, value} objects. Typically from the output of backtest_strategy. Needs at least 30 data points. Example: [{date: '2023-01-03', value: 100000}, {date: '2023-01-04', value: 100500}, ...] |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, and idempotent behavior. The description adds value by detailing the OLS regression process, factor interpretation, statistical significance thresholds, and the set of outputs (alpha, loadings, R-squared, residual vol). This goes beyond what annotations provide.
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 concise and well-organized: it opens with the main action, breaks down factors, explains significance, lists outputs, and places the tool in context. Every sentence is informative with no redundancy.
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 the tool's complexity (statistical regression with multiple outputs), the description covers inputs, process, outputs, and usage scenario. The presence of an output schema (as indicated by context signals) reduces the need to detail return values, leaving the description sufficiently complete for an agent to invoke correctly.
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% and the equity_curve parameter is well-described with format, source, requirement, and example. The tool description does not add further parameter details beyond what the schema already provides, so a baseline of 3 is appropriate.
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 clearly states the tool decomposes returns into Fama-French factors using OLS regression, lists four factors, and explains alpha. It also specifies its place relative to siblings: 'Use this after backtest_strategy to understand WHERE your returns come from.' This distinguishes it from the sibling tools.
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 explicitly directs usage after backtest_strategy and notes the requirement of at least 30 data points in the equity curve parameter description. It does not explicitly cover when not to use or contrast with screen_stocks, but the context is clear enough for typical use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Screen a stock universe with quantitative filters. Returns ranked candidates with scores and metrics.
Use this tool when you need to find stocks matching specific criteria — value stocks, momentum leaders, quality companies, or multi-factor ranked candidates. Supports 7 screen types across 3 universes (S&P 500, Russell 2000, Nasdaq 100).
After screening, use backtest_strategy to test the screen as a trading strategy, or factor_analysis to understand the factor exposures of the selected stocks.
| Name | Required | Description | Default |
|---|---|---|---|
| universe | No | Stock universe to screen. Options: sp500, russell2000, nasdaq100 | sp500 |
| screen_type | No | Type of screen to run. Options: fundamental_screen (filter by PE/ROE/debt), quality_screen (filter by ROE/margins), momentum_screen (rank by price momentum), value_screen (rank by valuation), factor_model (multi-factor ranking), technical_signal (RSI/SMA/Bollinger), mean_reversion (z-score below threshold) | fundamental_screen |
| config | No | Screen-specific configuration. Examples: fundamental_screen: {pe_lt: 15, roe_gt: 12}. momentum_screen: {lookback_days: 200, top_pct: 0.2}. value_screen: {pe_lt: 20, top_n: 30}. factor_model: {weights: {value: 0.3, momentum: 0.3, quality: 0.2, volatility: 0.2}, top_n: 20}. mean_reversion: {lookback_days: 60, z_threshold: -1.5}. All parameters are optional — sensible defaults are used. | |
| date | No | Date for the screen in YYYY-MM-DD format. Defaults to most recent trading day. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description aligns by describing a read-only screening operation that returns results without side effects. The description adds context about the return format (ranked candidates with scores and metrics) and supported screen types and universes, going beyond what annotations provide.
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 four sentences, front-loaded with the core action and output, followed by usage context and scope, and ending with guidance on next steps. Every sentence adds value with no redundancy or filler.
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 the existence of an output schema and complete schema descriptions, the description covers purpose, usage, scope, and follow-up tools. It provides sufficient context for an agent to understand when and how to use the tool.
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%, meaning all parameters are already well-documented in the input schema with their types, defaults, and examples. The description does not add significant new information about parameters beyond the schema, so it meets the baseline expectation for a high-coverage schema.
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 states the tool screens a stock universe with quantitative filters and returns ranked candidates. It clearly identifies the action (screen), resource (stock universe), and output (ranked candidates with scores). It also distinguishes from sibling tools by mentioning backtest_strategy and factor_analysis as follow-ups.
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 explicitly says to use this tool to find stocks matching specific criteria, listing value, momentum, quality, or multi-factor. It provides guidance on what to do after screening (use backtest_strategy or factor_analysis). However, it does not explicitly state when not to use it or mention alternative tools for other tasks, though the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.2.0backtest_strategyfactor_analysisscreen_stocksEach tool has a distinct purpose: screen_stocks finds candidates, backtest_strategy tests strategies, factor_analysis decomposes returns. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case: backtest_strategy, factor_analysis, screen_stocks.
Three tools is minimal but well-scoped for the quantitative finance pipeline. Covers screening, backtesting, and analysis without excess.
The tools form a coherent workflow (screen → backtest → factor analysis). Minor gaps exist, such as no data retrieval or custom factor tools, but the core pipeline is complete.
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