Skip to main content
Glama
zomma-dev

QuantContext

by zomma-dev

screen_stocks

Read-onlyIdempotent

Find stocks matching specific criteria using quantitative filters across S&P 500, Russell 2000, or Nasdaq 100. Choose from 7 screen types to rank candidates by value, momentum, quality, or multi-factor scores.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
universeNoStock universe to screen. Options: sp500, russell2000, nasdaq100sp500
screen_typeNoType 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
configNoScreen-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.
dateNoDate for the screen in YYYY-MM-DD format. Defaults to most recent trading day.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zomma-dev/quantcontext-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server