boothcheck

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

Annotations already cover readOnly/destructive hints. The description adds transparency about the output being derived from reverse-DCF and that it is a comparison, not a recommendation. It also clarifies the scope to US stocks, which is useful beyond the schema.

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 core purpose, and the second sentence adds a clarifying exclusion. Every word earns its place; no filler or redundancy.

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 (two parameters, no output schema), the description gives sufficient context about what the output represents (implied growth, duration, margin input). It could mention potential limitations or edge cases, but for this scope it is complete enough.

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% with descriptions for both tickerA and tickerB, including examples. The description's mention of 'US stock prices' reinforces the US ticker requirement but adds little beyond the schema's own parameter 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 the tool performs a side-by-side comparison of what two US stock prices imply about growth and duration via reverse-DCF. It explicitly distinguishes itself from sibling tools by noting it provides no house fair value or rating, making its purpose unique.

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 on when to use the tool: when comparing assumptions embedded in two prices, and it explicitly excludes house fair value or rating. It does not name alternative tools, but the 'No Boothcheck house fair value or rating' line implicitly directs users away for valuation opinions.

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 declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context: access requirements for full vs. public reports, and the explicit content boundary that no fair value, target, or rating is included. 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 extremely concise and well-structured: two sentences cover purpose, access, and exclusions without any redundant information. 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?

Given the tool's simplicity (one parameter, no output schema, safe read-only behavior), the description covers purpose, access, and content boundaries sufficiently. It implies a return structure ('bullet takeaways' and 'which sections exist') that is clear enough for an agent to use it 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?

Schema coverage is 100% and the only parameter (ticker) has a clear description. The tool description does not add extra parameter-level detail beyond stating the report context, so 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 returns bullet takeaways of a boothcheck narrative research report and lists which sections exist. It also explicitly says it never provides fair value, target, or rating, distinguishing it from sibling tools like whats_priced_in or 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 implies usage for retrieving report summaries and clarifies access conditions (keyless for public reports, Founding Access key for the full library). It also states what the tool does not provide ('Never a fair value, target, or rating'), serving as an exclusion, though it does not name alternative tools explicitly.

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 establish readOnlyHint=true, so the safe-read nature is covered. The description adds meaningful context beyond annotations: the content is historical research, non-predictive filing notices, failed and killed ideas, and never individualized recommendations. This helps an agent set expectations and avoid over-interpreting output, which goes beyond simple read-only metadata.

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 front-loaded with the core use case and ends with an important limitation phrase. It packs a lot into one long sentence and a few follow-ups, and while the first sentence is a dense list, every clause contributes a distinct navigation path or boundary. It is organized and readable, though slightly verbose with several overlapping categories.

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, the description cannot rely on structured output documentation, but it still supplies enough domain boundary, mode routing, and caveats for an agent to call the tool appropriately. It does not detail exact return shapes, but for a read-only record-and-notice tool with three optional parameters, this is sufficient guidance.

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%, so the baseline is fair at 3. The description adds semantic meaning to the enum modes themselves: overview as the validated strategy and governance record, open as detections, notices as non-predictive filing boards, etc. The schema alone would expose only opaque mode names, so this is a clear value-add.

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 opens with a specific directive: "Use this for any question about Boothcheck strategies, signals..." It names a clear resource and scope, and the mode list (overview, open, history, signals, notices, graveyard) tells an agent exactly what domain it covers. This distinguishes the tool from its siblings, which appear to cover pricing and coverage rather than strategy records.

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 gives explicit guidance: use this tool for Boothcheck strategy/signal questions, and it even says "never individualized recommendations." It also maps each mode to its use case, e.g., "graveyard for failed research." It does not explicitly name sibling tools as alternatives, but it clearly defines the tool's territory and a notable when-not case.

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 declare readOnlyHint=true and destructiveHint=false, so safety is known. The description adds useful behavior context: pagination at 100 and the scope (US tickers with live records). However, it does not mention ordering, error cases, or what happens on out-of-range pages, so it provides moderate value beyond 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 concise sentences: the first establishes purpose and content, the second states pagination. No filler or repetition. Information is front-loaded and every sentence 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 paginated list tool with one optional parameter, the description is nearly complete. It explains what items are included and the pagination behavior. The lack of an output schema is mitigated by the description's clear mention of the returned fields (ticker and resolution level). Minor omissions like total count or default page are not critical.

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 fully documents the single page parameter with type, minimum, and description (including '100 tickers per page'). The description reinforces the pagination size but adds no new information beyond what the schema already states, so the baseline 3 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 explicitly states the tool's purpose: listing every US ticker with a live implied-expectations record and its resolution level. This clearly distinguishes it from sibling tools that analyze or compare data, making it the inventory/list endpoint.

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 this tool is the authoritative source for checking what coverage exists ('honest inventory'), but it does not explicitly state when to use it over alternatives like get_report_summary or most_stretched. No exclusion conditions or when-not-to-use 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 declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful context by stating it is not a sell list and clarifying the output includes 'growth and duration'. This goes beyond the annotations without contradicting them, though it omits data-source or timing 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 two sentences, front-loads the core action, and every clause earns its place. It efficiently covers purpose, method, output, and a caution without redundancy.

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 simple tool (one optional parameter, no output schema), the description fully covers sufficient context: what it does, what it returns, and what it does not do. The reverse-DCF methodology note adds depth, and the no-output-schema gap is compensated by the return-shape description.

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 'limit' parameter, so the schema already documents the range and default. The description references 'top N' but provides no additional parameter-level detail beyond what the input 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?

Description uses a specific verb ('Rank') and identifies the resource ('US stocks') plus the metric ('growth baked into price'), clearly distinguishing it from a generic list or sell recommendation. The parenthetical definition ('implied operating-income growth from a reverse-DCF read') adds precision without ambiguity.

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 conveys clear usage context: it ranks by 'how much growth is already baked into price' and explicitly states it is 'Not a sell list', so an agent knows not to use this to get sell recommendations. However, it doesn't name sibling alternatives like compare_priced_in or whats_priced_in, so the 'when to use vs alternatives' is only partially explicit.

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, and the description adds valuable behavioral context: reverse-DCF methodology, SEC EDGAR data source, coverage of about 2,000 US tickers, and what it intentionally does not return. No contradiction with annotations; the added context goes beyond the schema.

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 front-loaded with the core purpose, followed by exclusions and scope, all in three sentences. It is concise, though phrases like 'boothcheck' and 'where the route holds one as an input' are slightly confusing and could be cleaned up for clarity.

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 reverse-DCF tool with no output schema, the description covers key output dimensions (growth, years, terminal margin), data source, and coverage. It does not specify exact return format or behavior for uncovered tickers, but the core context is sufficiently complete for selection and 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?

The schema fully describes the single ticker parameter, but the description adds meaning by specifying US stocks, coverage scope (~2,000 tickers), and that the analysis is based on current price. This helps the user understand ticker validity and potential coverage limitations.

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 inverts a US stock's current price into implied operating-income growth, years sustained, and terminal margin, versus actual delivered results. This is a specific, unique purpose that distinguishes it from siblings like compare_priced_in and most_stretched, which likely focus on comparison and ranking.

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?

It provides clear context for when to use the tool: to judge the bet a price implies, and explicitly lists exclusions (no fair value, price target, or rating). However, it does not explicitly name alternative tools or state when to prefer a sibling, so it lacks full alternative guidance.

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