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anconina

yfinance-mcp-ts

by anconina

get_screener

Run predefined stock screeners like day gainers or most actives to return top results in a compact text table or JSON for quick market research.

Instructions

Runs a stock screener and returns top results as a compact table. Use list_screeners first to find screener names. Text default; set format=json for structured data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of results to return (default: 10)
fieldsNoField names to include per quote in JSON output (default: ~24 common fields including symbol, price, change, volume, marketCap, PE ratios, 52wk range, sector). Ignored for text format.
formatNoOutput format (default: text)
screenerYesScreener name (e.g., "day_gainers", "day_losers", "most_actives", "undervalued_growth_stocks")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.6

TDQS

A3.8/5.0
Behavior3/5

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

No annotations, so the description carries the full burden. It discloses the default text format, the JSON alternative, and the default count of 10 indirectly via schema. It doesn't state rate limits, caching, or what happens with an invalid screener name, but for a read-only screener invocation this is adequate if minimal.

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?

Two tight sentences, front-loaded with the core action, then the dependency and format options. No waste.

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

Completeness4/5

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

Given 4 params with full schema descriptions and no output schema, the description covers the key behavioral facts an agent needs: prerequisite call, default format, and JSON option. It could mention what the compact table contains or error behavior, but it's sufficient for correct invocation.

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 coverage is 100%, so the schema already documents count, fields, format, and screener with examples and defaults. The description adds only the format default and the count=10 default, which duplicates schema content. Baseline 3 applies when schema does the heavy lifting.

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?

States a specific verb (runs/returns) and resource (stock screener) and clarifies the output form (compact table). It doesn't differentiate from siblings like get_trending or list_screeners beyond naming list_screeners as a prerequisite, but the purpose is unambiguous.

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?

Explicitly directs the agent to call list_screeners first to find screener names and explains the default format versus format=json. Lacks explicit when-not-to-use guidance (e.g., distinguishing from get_screener_info), but the prerequisite is clear.

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