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

With no annotations provided, the description thoroughly covers all behavioral traits: read-only, stateless, idempotent, rate limits (free 10/day, pro unlimited), authentication requirement, error handling, and data privacy. 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?

The description is well-structured with sections for behavior, usage, arguments, and transparency. While it is lengthy, each section adds necessary context, and the information is front-loaded with the core purpose and output.

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 annotations and only two parameters (one required), the description covers all essential aspects: output format, side effects, authentication, rate limits, error handling, and privacy. The presence of an output schema reduces the need to detail return values, but the description already mentions the output components.

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 0%, but the description includes an 'Args' section with minimal descriptions: 'text' is 'The text to analyze or process', and 'api_key' is 'The api key to analyze or process' – the latter is redundant and uninformative. It fails to explain the purpose or usage of the api_key parameter beyond being a key.

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 analyzes sentiment, providing score, label, and confidence. It distinguishes from siblings like batch_analyze, compare_sentiments, and extract_emotions by focusing on single text sentiment analysis.

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 explicit 'When to use' and 'When NOT to use' sections, but the 'When to use' is somewhat generic ('structured analysis or classification of inputs against established frameworks or standards'), lacking specific mention of sentiment analysis. It does caution against real-time production use without human review.

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 fully covers behavioral traits: it declares read-only, stateless, idempotent, and no side effects. It details authentication (basic vs pro), rate limits (10/day free, unlimited pro), error handling, and data privacy. This exceeds the burden required for transparency.

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 well-structured with clear sections, front-loading the main purpose. While slightly verbose, each section (behavior, usage, args, transparency) adds distinct value and avoids unnecessary 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 tool's batch processing nature and two parameters, the description covers behavior, usage, and transparency thoroughly. Since an output schema exists, return values are not needed. The description lacks examples but is otherwise complete for selecting and invoking the tool 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?

The 'Args' section adds minimal value beyond the schema, but the description compensates partially by explaining the delimiter for texts and noting authentication requirements for API key. Given 0% schema coverage, more detail on input formatting or examples would improve semantics.

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 analyzes sentiment for multiple texts at once, specifying the '|||' delimiter. It distinguishes itself from siblings like analyze_sentiment (single text) and compare_sentiments (comparison) by focusing on batch processing, though it doesn't explicitly contrast them.

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 explicit 'When to use' and 'When NOT to use' sections, guiding the agent towards structured analysis and away from real-time decision-making without human review. However, it could better compare with sibling tools to clarify when this batch tool is preferred over single analysis.

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?

Provides a dedicated 'Behavioral Transparency' section covering side effects, authentication, rate limits, error handling, idempotency, and data privacy. Since no annotations exist, the description fully bears this burden and does so comprehensively.

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?

Well-structured with sections but contains redundancy (e.g., stateless idempotency repeated in 'Behavior' and 'Behavioral Transparency'). Some sentences are generic (e.g., 'The text a to analyze or process'). Could be more concise.

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?

Covers behavior, usage, and transparency well, but lacks description of the output schema (exists but unaddressed). The 'When to use' section is generic and does not fully specify the tool's niche. Adequate but has 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?

With 0% schema description coverage, the description only repeats parameter names in the 'Args' section without adding meaningful details like format, constraints, or examples. It fails to compensate for the lack of schema descriptions.

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 'Compare the sentiment of two texts side by side', with a specific verb and resource. It distinguishes from sibling tools like analyze_sentiment (single text) and batch_analyze (batch) by focusing on side-by-side 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?

Includes explicit 'When to use' and 'When NOT to use' sections, stating it's for structured analysis/classification and not for real-time production decisions without human review. However, it does not directly mention alternatives like sibling tools.

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?

In the absence of annotations, the description thoroughly covers side effects, authentication, rate limits, error handling, idempotency, and data privacy in a dedicated 'Behavioral Transparency' section. It is comprehensive and clearly communicates the tool's 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 well-structured with clear sections and front-loaded purpose. Some repetition occurs between the initial bullet points and the detailed 'Behavioral Transparency' section, but overall it is efficient and easy to read.

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's simplicity (2 parameters, no nested objects, output schema present), the description covers all necessary aspects: purpose, usage, parameter semantics, and extensive behavioral details. It is fully complete for an agent to use 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?

The description provides brief parameter descriptions for text and api_key, but they add little beyond the schema (e.g., just restating name and type). With 0% schema coverage, the description barely compensates; the documentation is minimal.

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 detects emotions in text and returns intensity scores. It distinguishes from siblings like analyze_sentiment by focusing specifically on emotions.

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 dedicated 'When to use' and 'When NOT to use' sections, providing clear context for when to apply the tool. However, it does not explicitly compare to sibling tools like batch_analyze or compare_sentiments.

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