boothcheck

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

Annotations indicate read-only behavior. The description adds transparency by detailing the reverse-DCF inversion process and what is compared (growth, duration, margin). It confirms no destructive effects. There is no contradiction with annotations.

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 two sentences, front-loading the core purpose and then clarifying boundaries. Every word adds value; no unnecessary repetition or fluff.

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 two-parameter tool with no output schema, the description covers the key outputs (implied growth, duration, margin) and clarifies what it does not provide. It could potentially mention output format or data scope, but it's largely complete given 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?

Schema description coverage is 100%, with both parameters described as US tickers. The description mentions 'US stock prices' but does not add new meaning beyond the schema. 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 compares assumptions (implied growth, duration, margin) embedded in two US stock prices via reverse-DCF. It explicitly says what it does not do (no fair values, no ratings), distinguishing it from potential alternatives and sibling tools like 'whats_priced_in' and 'most_stretched'.

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 provides context for when to use the tool: comparing assumptions between two US stock prices. It includes an exclusion (no fair values/ratings), which helps guide appropriate use. However, it does not explicitly reference sibling tools or provide direct 'when not to use' guidance, so it's slightly less than perfect.

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?

Beyond annotations (readOnlyHint, destructiveHint), the description adds that the tool never returns fair value, target, or rating, and explains access limitations. This adds valuable 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 two sentences, front-loaded with the main purpose, and every sentence adds value. No redundant or 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 tool with one parameter and no output schema, the description adequately covers purpose, access constraints, and content exclusions. It could mention output format but is sufficient for agent understanding.

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 parameters (ticker) with its own description. The description does not add additional semantic information about the parameter, so baseline 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 it provides bullet takeaways and section list of a boothcheck narrative research report. It distinguishes itself by noting public vs member access, though it could more explicitly differentiate from sibling tools.

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 context (public vs member) and explicitly states what the tool does NOT return (fair value, target, rating). However, it does not provide guidance on when to use this tool versus its siblings like 'compare_priced_in' or 'list_coverage'.

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?

Annotations already indicate read-only and non-destructive. Description adds detail about the data returned (tickers, resolution levels) and pagination, with 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?

Two concise sentences, front-loaded with purpose, no wasted 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?

Low complexity tool with one optional param and no output schema; description adequately covers what is returned and pagination.

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 description coverage is 100%, and the page parameter is fully described in schema. Description repeats pagination info but adds no new semantic value.

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?

Clearly states it lists coverage of US tickers with implied-expectations records and resolution levels, distinguishing it from sibling tools that compare or summarize.

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?

Purpose is implied for inventory queries, but no explicit when-to-use or alternatives guidance is provided.

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?

Annotations already indicate read-only and non-destructive behavior. The description adds value by detailing the output fields (growth, duration, margin) and clarifying that it is a ranking, not a recommendation. No contradictions with annotations.

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 two sentences, with the first sentence front-loading the core purpose and methodology. Every phrase adds value, and there is no waste.

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 tool has one parameter and no output schema. The description explains the ranking concept, methodology, and output fields (growth, duration, margin). It adequately covers what the agent needs to understand the tool's use, though slightly more detail on output format could elevate it.

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% for the single parameter 'limit', with a clear schema description. The tool description does not add additional meaning beyond what the schema provides, so per the rubric baseline is 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 'Rank' and clearly identifies the resource 'US stocks'. It explains the methodology (reverse-DCF) and distinguishes from siblings by focusing on ranking by implied growth. The title 'Most stretched bets' aligns well with the description.

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 provides clear context on what the ranking represents ('where the bar to clear is highest') and explicitly states it is not a sell list. However, it does not explicitly mention when to use this tool versus its siblings (compare_priced_in, whats_priced_in) or provide direct alternatives.

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?

Description explicitly states it never returns fair value, price target, or buy/sell rating, which is critical behavioral constraint beyond readOnlyHint annotation. Also mentions data source (SEC EDGAR) and method (reverse-DCF). No contradiction with annotations.

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?

Description is efficient, front-loaded with purpose, each sentence adds value. No 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 tool with one parameter and no output schema, description adequately explains what outputs are (growth rate, years, margin) and what is not returned. Could specify output format but not essential for invocation.

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?

Only one parameter (ticker) with schema description. Description adds coverage limitation (~2,000 US tickers) beyond schema, but schema already defines format. Schema coverage is 100%, so baseline 3; description adds moderate extra value.

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 it inverts a US stock's price into implied operating-income growth rate, years sustained, and operating margin. It distinguishes from siblings like compare_priced_in by stating it does not return fair value or ratings.

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?

Explanation includes when to use (to see what price assumes) and when not (it doesn't give ratings). Also specifies coverage of ~2,000 US tickers, guiding proper use.

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