ai-prompt-optimizer

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

No annotations are provided, so the description carries the burden. It clearly lists the analytical outputs, but does not explicitly disclose behavioral traits such as side effects, safety, or external calls. However, 'analyze' implies a non-mutating, read-only operation, which partially covers 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 a single, front-loaded sentence with no wasted words. It immediately identifies the action and resource, then lists concrete outputs, making it highly concise and scannable.

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 tool with one parameter and no output schema, the description covers inputs and expected outputs well. It lists the analysis dimensions, but does not specify the exact response format. Given the tool's simplicity, this is adequate and nearly complete.

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% and describes 'prompt' as 'Prompt to analyze'. The description adds context by specifying the analysis dimensions, but does not provide additional syntax, format, or constraint details 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?

The description uses a specific verb ('Analyze') and resource ('a prompt'), and enumerates the analysis dimensions (clarity score, filler patterns, ambiguity, improvement suggestions). This clearly distinguishes it from sibling tools like compare_prompts or optimize_prompt.

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 its use for analyzing a single prompt, but it does not explicitly state when to use it over alternatives or provide exclusions. Sibling tool names offer context, but the description itself lacks direct guidance.

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 burden. It discloses the return type (winner and token delta) but does not mention side effects, safety, or computation details. The partial transparency is helpful but lacks deeper behavioral context.

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 extremely concise, consisting of two short sentences that state the core function and expected output. Every word earns its place, with no redundancy or filler.

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 two-parameter comparison tool, the description adequately covers the purpose and output. It lacks usage guidance and details on how clarity score is computed, but given the tool's simplicity, it is reasonably complete. The absence of an output schema makes the return description important, and it is provided.

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 has no parameter descriptions (0% coverage). The description clarifies that prompt_a and prompt_b are the two prompts being compared, which adds meaning, but it does not elaborate on ordering, validation, or interpretation of these parameters beyond their names.

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 compares two prompts and specifies the output (winner by clarity score and token delta). It is distinct from sibling tools like analyze_prompt (single prompt analysis) and estimate_tokens (token counting).

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 implication is clear: use this when you need to compare two prompts. However, the description does not explicitly state when to use this over alternatives like analyze_prompt or optimize_prompt, nor does it provide exclusions or alternative recommendations.

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 only states the function and input types, with no mention of read-only behavior, output format, tokenizer specifics, or side effects. This is a notable gap for an agent deciding whether to invoke the tool.

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, front-loaded sentence that states the core function with no redundant words. Every word adds value, and the sentence is compact yet clear.

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?

With no output schema and no annotations, the description must explain what the tool returns and any relevant constraints. It only states 'estimate token count', leaving the output shape (e.g., single number vs. per-text array) and tokenization behavior unspecified. This is incomplete for a tool that produces a quantitative result.

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%: both 'texts' and 'prompt' have descriptions. The description adds the 'or' relationship between the two parameters, but it does not clarify mutual exclusivity or behavior when both or neither are provided. Since the schema already documents each parameter, the description provides marginal added semantics, warranting 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 uses a specific verb ('Estimate') and clearly identifies the resource ('token count') and its input scope ('a prompt or batch of texts'). This distinct purpose is unambiguous and easily differentiated from siblings like analyze_prompt or optimize_prompt, which target other aspects of prompt handling.

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 when token estimation is needed, but it does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or prerequisites. Sibling tools exist but no cross-references or guidance is provided, so the context is only implicit.

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 full responsibility for behavioral disclosure. It only states 'Check API health' without indicating what the check involves, what response format is expected, or potential side effects.

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, direct sentence that efficiently conveys the tool's purpose. No wasted words, and the structure is appropriate for such 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?

Despite the tool's simplicity, the absence of an output schema and any description of return values or evaluation criteria leaves the agent without critical context. It is unclear what a 'health check' returns or what constitutes healthy status.

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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the empty schema fully covers this aspect.

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 verb 'check' plus resource 'API health' clearly states the tool's function. It distinguishes itself from sibling prompt-analysis tools, though it could be more specific about which API and what 'health' entails.

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 provided on when to use this tool or alternatives. The description does not mention prerequisites or exclusions, leaving the agent to infer usage context.

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?

The description explains the transformation behavior and output (token savings) but does not disclose potential side effects, limitations, or statelessness. With no annotations provided, full behavioral burden falls on the description, which lacks these details.

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, focused sentence that efficiently conveys the core functionality without unnecessary words.

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, the description captures the essential behavior and return value, but it does not specify the exact output structure (e.g., whether the optimized prompt is also returned). This is a minor gap given the lack of an output schema.

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 single parameter 'prompt' is clearly described in the schema, and the tool description adds context about the optimization operations. Since schema coverage is 100%, the description provides marginal additional semantic value, warranting a 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 clearly states the tool's function: optimizing a prompt via specific transformations (removing filler words, compressing verbosity) and returning token savings. This distinguishes it from sibling tools like analyze_prompt and estimate_tokens, which serve different purposes.

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 prompt optimization but does not explicitly state when to choose this over alternatives. No references to sibling tools or exclusion conditions are provided, leaving the agent to infer from the name.

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