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quality-screener

Quality Screener MCP Server

scores_list

Read-only

List scored stock tickers with filters for sector, country, exchange, market cap, and quality score range. Get only the tickers you need.

Instructions

List scored tickers with optional filters. Market caps are in USD.

Rows are slimmed for MCP by default (duplicates as ticker strings, description truncated); pass full_rows=True for the raw backend payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
tickerNo
sectorsNo
sort_byNoquality_score
countriesNo
exchangesNo
full_rowsNo
max_scoreNo
min_scoreNo
currenciesNo
industriesNo
sort_orderNodesc
include_duplicatesNo
max_market_cap_usdNo
min_market_cap_usdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and openWorldHint annotations, the description discloses meaningful behavior: default rows are slimmed, duplicates appear as ticker strings, descriptions are truncated, and full_rows=True returns the raw backend payload. It also clarifies that market caps are in USD, adding value beyond the annotations.

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 short, front-loaded with the core purpose, and every sentence adds useful information. The market-cap unit note and the full_rows payload distinction are high-value details with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity — 16 parameters, no output schema, and no parameter descriptions — the description is incomplete. It explains the general list behavior and row-slimming but does not describe pagination, sort behavior, filter value formats, or the actual row fields returned, limiting an agent's confidence in invoking it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the 16 parameters. It only clarifies that market-cap parameters are in USD and explains full_rows; the rest, including limit, offset, ticker, sectors, sort_by, sort_order, min_score, and max_score, are left to inference from parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific action and resource: 'List scored tickers with optional filters.' It adds distinguishing details like market caps in USD and the default slimmed row format, making the purpose clear even though it does not explicitly contrast with sibling list tools like scores_top or scores_for_tickers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to choose scores_list over sibling tools such as scores_top, scores_for_tickers, or scores_show. The only conditional advice is the full_rows=True switch, which explains payload shape rather than tool selection, so there is no when-to-use or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.