ai-prompt-optimizer

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

No annotations are present, so the description carries full burden. It states the tool analyzes for clarity score, filler patterns, ambiguity, and suggestions, but omits whether it is read-only, requires authorization, or has side effects. For a no-annotation tool, this is insufficient.

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 with no redundant information. It front-loads the purpose and key outputs efficiently.

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 a simple input (one string) and no output schema, the description adequately hints at the return content (clarity score, filler patterns, ambiguity, improvement suggestions). However, it could specify whether the output is structured or free text.

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 already covers the single parameter with a clear description ('Prompt to analyze'). The tool description adds no additional parameter-level context, so only baseline score applies.

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 a prompt for clarity score, filler patterns, ambiguity, and improvement suggestions. It distinguishes itself from siblings like 'compare_prompts' and 'optimize_prompt' by focusing on analysis rather than comparison or optimization, though the distinction from 'optimize_prompt' could be sharper.

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 versus alternatives like 'optimize_prompt' or 'compare_prompts'. The description lacks any conditional usage instructions or exclusions.

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 must disclose behavioral traits. It mentions returning a winner and token delta but fails to state side effects, read-only nature, or any required permissions. Lacks detail.

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 very short (one sentence) and to the point, but could be more informative without becoming verbose. There is no wasted text.

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 comparison tool with two string parameters and no output schema, the description provides the basic idea. However, it lacks explanation of 'clarity score', output format, and any constraints. Adequate but not comprehensive.

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% and description adds no meaning beyond parameter names. It does not explain how prompt_a and prompt_b are used, nor what 'clarity score' or 'token delta' mean in relation to parameters.

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 returns a winner based on clarity score and token delta. It distinguishes from siblings like analyze_prompt or optimize_prompt, which have 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?

No guidance on when to use this tool versus alternatives such as analyze_prompt or optimize_prompt. The description does not specify context or prerequisites.

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 burden for behavioral disclosure. It implies a read-only operation (estimating), which is appropriate, but does not reveal any edge cases, limits (e.g., max input length), or how estimation is performed. It provides basic transparency but lacks depth.

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, well-structured sentence of 10 words. It is front-loaded with the core action and resource, with no wasted words. Every token contributes meaning.

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?

The description is too brief given the lack of output schema and annotations. It omits details like return format (e.g., integer, error handling), behavior with conflicting inputs, and differentiation from siblings. The tool is simple, but the description fails to cover essential context for reliable agent usage.

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 parameters have descriptions in the schema), providing a baseline of 3. The description adds minimal value beyond the schema, only implying that either 'prompt' or 'texts' can be used. It does not clarify mutual exclusivity or behavior when both are provided.

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: estimating token count for a prompt or batch of texts. It uses a specific verb ('estimate') and resource ('token count'), and distinguishes it from siblings like analyze_prompt by focusing narrowly on counting tokens.

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 estimate_tokens versus sibling tools like analyze_prompt or compare_prompts. It only states what it does, leaving the agent to infer usage context without any explicit exclusions or alternative suggestions.

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 provided, so description must disclose behavioral traits. It only states 'Check API health' without specifying whether it is read-only, what it tests, or what response to expect. Lacks critical 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?

Extremely concise at 3 words, but the brevity sacrifices informativeness. While not verbose, it misses important details that could be added without much length.

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 and no annotations, the description must fully describe tool behavior. It fails to mention response format, error conditions, or any side effects. Incomplete for a tool that requires 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?

No parameters exist (0 params, 100% schema coverage). Baseline is 4. The description does not add information beyond the empty schema, but no additional parameter details are needed.

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 'Check API health' clearly states the tool's action (check) and resource (API health). It is distinct from sibling tools which focus on prompt analysis, thus effectively differentiates.

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 on when or when not to use this tool. No mention of prerequisites or alternatives. For a health check, it should indicate it's for verifying API availability, but the description provides no 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?

Without annotations, the description carries the full behavioral disclosure burden. It explains the tool will remove filler words and compress verbosity, and return token savings, but does not clarify whether the core meaning is preserved, if the operation is reversible, or what happens to extremely short prompts. A score of 3 indicates adequate but not comprehensive 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 sentence that efficiently conveys the tool's purpose and method without any unnecessary words or redundant information.

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, single-parameter tool with no output schema, the description provides sufficient context: what it does and what it returns (token savings). However, it does not specify the exact return format (e.g., whether it returns the optimized prompt in addition to savings), which would be helpful. Overall, adequate for the tool's simplicity.

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 only parameter 'prompt' is described in the schema as 'Prompt to optimize'; the description adds no additional semantic detail about the parameter. Since schema coverage is 100%, the baseline of 3 is appropriate.

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 action ('Optimize a prompt') and method ('by removing filler words, compressing verbosity'). It distinguishes from siblings like 'analyze_prompt' (analysis, not optimization) and 'estimate_tokens' (estimation only).

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 reducing prompt length and token count but provides no explicit guidance on when to use or not use this tool versus alternatives like 'compare_prompts' or 'analyze_prompt'. No exclusion criteria or prerequisites are mentioned.

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