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

With no annotations, the description carries full burden. It details the return values (english/chinese aggregates and divergence) and explains the behavioral trait that large divergence often precedes mood shifts. It does not mention destructive behavior (none needed) or auth/rate limits, but is transparent about output and interpretation.

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 three sentences, front-loaded with purpose. It is clear but slightly verbose in explaining divergence meaning and examples. Every sentence adds value, but could be tightened.

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 no output schema, the description fully explains return values and divergence formula. It covers purpose, usage context, parameters, and behavioral insights. For a 2-parameter tool with 100% schema coverage, it leaves no gaps.

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%, so baseline is 3. The description adds value for ticker by listing useful examples (Taiwan-listed, US semis, ADRs), but for window_hours it largely repeats schema info (default, max, min). Overall, schema already documents parameters well.

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 explicitly states the tool compares English vs Traditional Chinese news sentiment side-by-side with a divergence score, and specifies its purpose for cross-market arbitrage signal detection. It is distinct from siblings like get_sentiment (single language) and get_sentiment_batch (batch for one language).

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 explains when to use (for cross-market arbitrage) and provides context about US press leading Taiwan/Asia press, as well as interpreting divergence. It lacks explicit 'when not to use' or comparison with sibling tools, but the context is clear.

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 provided, the description carries full burden. It transparently describes the output format, sources (listing specific English and Chinese outlets), and coverage skew (US large-caps, Taiwan semis). It does not mention any destructive behavior or side effects, but as a read-only tool, this is acceptable. Lacks mention of rate limits or authentication needs.

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 concise and dense, covering purpose, output, sources, and usage in a few sentences. It is front-loaded with the main action. However, the 'Useful for' list could be integrated into a more structured format.

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 low complexity (2 parameters, no output schema), the description is remarkably complete. It explains the return value details, sources, and usage contexts. No output schema is needed as the description sufficiently describes the response.

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?

Input schema coverage is 100%, so schema already documents parameters. The description adds value by clarifying ticker case-insensitivity, length limits, and coverage bias, as well as window_hours default and max. This goes beyond the schema alone.

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's purpose: get aggregated financial news sentiment for a single stock ticker. It specifies the output details (score range, confidence, article count, distribution, and latest articles) and sources. This distinguishes it from siblings like get_sentiment_batch (multiple tickers) and compare_languages (language comparison).

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 includes a 'Useful for' section with concrete scenarios: gauging market mood before a position, monitoring post-earnings sentiment, explaining price moves. This provides clear usage guidance. However, it does not explicitly state when not to use the tool or mention alternatives beyond sibling tool names.

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, description fully discloses important behaviors: each ticker counts as separate request against daily rate limit, latest_3 articles omitted per result, and suggests fallback for full data. No contradictions.

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?

Four sentences, front-loaded with key purpose, each sentence adds distinct value: purpose, efficiency, rate limit, data truncation. No redundancy.

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 no output schema, description hints at response structure (articles omitted per result) but could explicitly state return format. Still adequately covers usage constraints and outcome.

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 covers both parameters with descriptions (100% coverage). Description adds no new parameter-specific details beyond schema. Baseline 3 is appropriate as schema does the work.

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?

Description clearly states 'Get sentiment for multiple tickers in one call' with specific verb and resource. Distinguishes from sibling get_sentiment by emphasizing it's for multiple tickers and more efficient for watchlists.

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?

Explicitly says when to use: 'More efficient than calling get_sentiment N times when you have a watchlist.' Also explains billing implications and data truncation, advising to call get_sentiment for full payload.

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