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GlacianNex

stockdata-mcp

by GlacianNex

fmp_screener

Filter stocks by sector, market cap, price, volume, dividend, and beta to discover securities meeting your fundamental and market criteria.

Instructions

Screen for stocks matching fundamental and market filters.

Call fmp_reference("sectors") or fmp_reference("industries") first if you need the exact spelling of a sector or industry value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
is_etfNo
sectorNo
countryNo
is_fundNo
exchangeNo
industryNo
beta_more_thanNo
beta_lower_thanNo
price_more_thanNo
price_lower_thanNo
volume_more_thanNo
volume_lower_thanNo
dividend_more_thanNo
dividend_lower_thanNo
is_actively_tradingNo
market_cap_more_thanNo
market_cap_lower_thanNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the transparency burden. 'Screen' implies a read-only query operation, but it does not explicitly disclose safety, rate limits, pagination, or how filters are combined. It is adequate but has noticeable gaps in behavioral detail.

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 two sentences, front-loaded with the core purpose, and includes a conditional usage hint. Every sentence earns its place with no filler or redundant detail.

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?

Despite having an output schema, the tool has 18 parameters and a minimal description. It does not explain how filters interact, whether limit caps results, or what numeric units are expected. This is a high-complexity tool, and the description is too sparse to fully guide correct use without external knowledge.

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?

The schema has 18 parameters with 0% description coverage, and the description offers almost no parameter-level meaning. It only advises using fmp_reference to get exact sector/industry values, which helps just two parameters. Units for numeric filters (price, market cap, beta, etc.) and filter combination logic are left unspecified.

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?

The description clearly states the tool screens for stocks based on fundamental and market filters. This distinguishes it from sibling tools like fmp_quote, fmp_profile, and fmp_market_movers by highlighting its screening nature, though it does not enumerate the specific filter dimensions.

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 gives a clear usage context: 'Screen for stocks matching fundamental and market filters.' It also provides a practical prerequisite—calling fmp_reference for exact sector/industry spellings. However, it does not explicitly mention when not to use the tool or compare it to alternative screening approaches.

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

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