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vlearner

mcp-etf-holdings

by vlearner

compare_etfs

Compare multiple ETFs side by side in one table covering category, AUM, expense ratio, dividend yield and YTD, 3-yr and 5-yr returns, replacing several single-fund lookups.

Instructions

Compare several ETFs side by side in a single table: name, category, AUM, expense ratio, dividend yield, and YTD / 3-yr / 5-yr returns.

Use this whenever the user mentions more than one ETF — it is one call instead of several etf_info calls, and the result is already tabular.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickersYesETF tickers to compare side by side, e.g. ['SPY', 'QQQ', 'VTI', 'SCHD']

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return shape (a comparison table with defined columns), which is useful, but says nothing about whether tickers must exist, how invalid tickers are handled, or any rate/permission constraints. For a low-risk read-only lookup this is adequate but not rich.

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 short paragraphs with zero waste: the output contents are front-loaded, then the usage rule and the alternative. Every sentence earns its place.

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?

An output schema exists, so the description needn't explain return values, yet it helpfully previews the table columns. Combined with the clear usage routing, an agent has everything needed to invoke it correctly; only edge-case behavior (invalid/missing tickers) is unaddressed.

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% and the single 'tickers' array parameter is documented in the schema with a concrete example (['SPY','QQQ','VTI','SCHD']). The description adds no format or cardinality guidance beyond what the schema already provides, so the baseline 3 applies.

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?

States a specific verb ('Compare') and resource ('ETFs'), and enumerates the exact output fields (name, category, AUM, expense ratio, dividend yield, returns), so the agent knows what it gets back. It is clearly distinguishable from the sibling etf_info, which it explicitly contrasts.

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

Usage Guidelines5/5

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

Gives an explicit trigger ('whenever the user mentions more than one ETF') and names the alternative it replaces ('one call instead of several etf_info calls'), with the added rationale that the result is already tabular. Nothing is left to inference.

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