stoxly

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

With no annotations, the description carries the full burden. It discloses that the tool scores against a 10-point checklist and returns the score, verdict, all metric values, and a link. This is clear behavioral context, though it does not explicitly state the absence of side effects (e.g., read-only nature).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense sentence that front-loads the core purpose and then efficiently lists the checklist and output components. No unnecessary words or repetition.

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

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

Given the absence of an output schema, the description thoroughly explains what the tool returns: score, verdict, every metric value, and a link. It also covers the tool's scope (US-listed ETF) and the exact evaluation criteria, making it complete for an AI agent to invoke correctly.

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

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

Schema coverage is 100% with a clear description of 'symbol' as a Yahoo Finance ticker. The tool description adds the important restriction that the symbol must be a US-listed ETF, which goes beyond the schema's examples and clarifies valid parameter usage.

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

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

The description clearly states 'Free analysis of a US-listed ETF' and enumerates the specific scoring checklist and return values. This distinguishes it from sibling tool analyze_stock by explicitly targeting ETFs rather than stocks.

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

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

The description implies usage for US-listed ETFs, which helps select this tool over analyze_stock, but it does not explicitly mention when not to use it or name the alternative. The guidance is inferred rather than stated.

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

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

With no annotations, the description carries the full burden of disclosure. It reveals the scoring methodology (10-point checklist), the output structure (score, verdict, metric values, link), and the 'free' aspect. It does not mention side effects or authentication, but as a read-only analysis tool, the description is sufficiently transparent about behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence that front-loads the primary purpose ('Free fundamental analysis') before elaborating on the checklist and return value. It contains no unnecessary words, though its length and dense list of metrics could be slightly more readable if split.

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

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

Given the tool has one parameter and no output schema, the description adequately covers the return value (score, verdict, metric values, link) and the analysis scope. It lacks details on limitations such as data latency or market coverage, but for typical use it is complete enough.

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

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

The input schema already provides full coverage for the sole 'symbol' parameter with examples. The description does not add any additional parameter semantics or conventions, so it does not exceed the baseline expected when schema coverage is high.

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

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

The description clearly states the tool performs stock analysis with a specific verb ('analyzes'), names the resource (publicly traded stock), and details the scoring system (10-point checklist). It inherently differentiates from the sibling analyze_etf by focusing on stocks, making purpose unambiguous.

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

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

The description establishes clear context by specifying 'publicly traded stock', implying this tool is for individual equities rather than ETFs. However, it does not explicitly reference the sibling tool or provide when-not-to-use exclusions, so it falls short of a 5.

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