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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It states that it analyzes text and returns patterns, which is basic but does not mention any side effects, prerequisites, error conditions, or performance characteristics. For a read-only analysis tool this is adequate, but not comprehensive.

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?

Two sentences with no redundancies. The core purpose and categories are front-loaded, and the return value is clarified in the second sentence. Every word earns its place.

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?

For a simple one-parameter tool with no output schema, the description covers the essential purpose. However, it does not describe the structure of the returned patterns, any limitations (e.g., language support), or how to interpret results, leaving an agent with only partial context for effective invocation and use.

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% (the single 'text' parameter has a description). The description adds that it analyzes text, which aligns with the schema but provides no additional nuance about format, encoding, or expected content beyond what the schema already states.

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?

States a clear verb (analyze) and resource (text for AI slop indicators), and explicitly lists three analysis categories. It distinguishes functionally from siblings (this analyzes, others provide rules/examples), though it doesn't name them directly, so it falls just short of a 5.

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?

No explicit when-to-use or alternative routing is provided. The purpose implies this tool is for analyzing text, while get_human_writing_rules and get_slop_examples would likely be used for reference materials, but the description does not state this or offer any conditions for selection.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys what the tool returns (rules) and how to apply them (as system-level instructions), which is reasonably transparent for a read-only retrieval tool. It doesn't disclose output scale, format, or how 'comprehensive' the rules are, but the essential behavior is clear.

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?

Two sentences with the purpose front-loaded before the usage direction. The first sentence delivers the core function and the second adds practical deployment guidance. No filler or repetition; appropriately sized for a simple tool.

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 — one optional parameter, zero required parameters, no output schema, no nested objects — the description covers the essentials: what the tool does and how to apply its results. The context parameter semantics are already in the schema. Nothing critical an agent needs to invoke it successfully is missing.

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% — the single optional 'context' parameter is fully documented in the schema with an example ('technical documentation', 'casual email'), so the schema already does the heavy lifting. The description adds nothing about the parameter beyond what the schema provides, meriting the baseline score of 3.

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 states a specific verb and resource: 'Get comprehensive rules for writing like a human and avoiding AI slop.' This is clearly a rules-retrieval tool, and it is reasonably distinguishable from siblings check_for_slop (detection) and get_slop_examples (examples). However, it doesn't explicitly name siblings or state how it relates to them, so differentiation is implicit rather than direct.

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 second sentence provides useful application guidance: 'Use these rules as system-level instructions for any text generation task.' This tells the agent when and how to deploy the output. However, it offers no exclusions or alternatives — it doesn't say when to prefer get_slop_examples or check_for_slop instead, leaving some selection burden on the agent.

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?

Since no annotations are provided, the description carries full responsibility for behavioral disclosure. It only states the core function without mentioning any restrictions, requirements, or side effects. There's no indication of output format, whether it returns a list, or any edge cases, providing minimal transparency beyond the obvious.

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?

A single, front-loaded sentence that clearly states the action and resource. No wasted words or redundancy, making it highly concise and easy to parse.

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?

For a tool with one optional parameter and no output schema, the description provides a basic understanding of its purpose. However, it doesn't specify the return format or any example output, which might be expected for a retrieval tool. Given the low complexity, it's adequate but leaves some room for more clarity.

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 schema covers 100% of the parameter description, including an enum for category with clear descriptions. The description adds 'categorized by type' which aligns with the category parameter, but offers no additional semantic value beyond what the schema already provides. Baseline 3 is appropriate given the high schema coverage.

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 fetches examples of common AI slop phrases and patterns, categorized by type. It specifies the verb 'get' and resource 'examples of slop phrases and patterns', making its purpose distinct from siblings like get_human_writing_rules and check_for_slop, though it doesn't explicitly name them as alternatives.

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?

No guidance is given on when to use this tool versus the sibling tools. It doesn't mention any exclusions, prerequisites, or alternative conditions, leaving the agent to infer that it's for retrieving examples. This is a significant gap given the tool's siblings have overlapping domains.

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