What a stock price is betting on: implied growth, duration, and margin, read from SEC filings.
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Average 4.1/5 across 5 of 5 tools scored.
Each tool has a distinct purpose: single-stock analysis, pairwise comparison, ranking, coverage listing, and report summary. No two tools overlap in function, so an agent can confidently select the right one.
Three tools follow a clear verb_noun pattern (compare_priced_in, get_report_summary, list_coverage), but most_stretched and whats_priced_in are descriptive phrases rather than imperative verbs, creating noticeable inconsistency.
With 5 tools, the server is well-scoped for its niche purpose. Each tool serves a distinct analytical need without unnecessary bloat.
The tool set covers the core workflows: analyzing a single stock, comparing two, ranking the market, listing coverage, and retrieving report summaries. The only gap is full report access, but that is intentionally restricted to members.
compare_priced_inCompare what two prices assumeASide-by-side of what two US stock prices are each betting on: implied growth and duration (plus any held margin input) from a reverse-DCF inversion of each price. No Boothcheck house fair value or rating — a comparison of the assumptions embedded in the two prices.
| Name | Required | Description | Default |
|---|---|---|---|
| tickerA | Yes | First US ticker, e.g. AMD | |
| tickerB | Yes | Second US ticker, e.g. NVDA |
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.
get_report_summaryReport summaryAThe bullet takeaways of a boothcheck narrative research report plus which sections exist. Public reports are summarized keylessly; the full cached library needs a member API key. Never a fair value, target, or rating.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US stock ticker symbol, e.g. NVDA |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint, so the safety profile is clear. The description adds significant context by noting keyless vs key-based access, the inclusion of section lists, and the explicit exclusion of valuation data. This goes beyond annotations to set expectations about content and access.
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 function, followed by access conditions and an exclusion statement. Every sentence adds unique value with no redundant phrasing.
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 low complexity (one parameter, no output schema), the description is quite complete. It covers content, access nuances, and limitations, leaving little ambiguity. A minor gap is not specifying the return structure (e.g., formatting of bullets/sections), but this is not essential for such a simple tool.
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 input schema has 100% coverage for the single parameter 'ticker' with a clear description, so the description does not need to add extra parameter detail. The baseline of 3 applies here, as the schema carries the full semantic load.
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 what the tool does: it returns bullet takeaways of a boothcheck narrative research report and indicates which sections exist. It distinguishes itself from siblings like compare_priced_in or most_stretched by focusing specifically on report summaries, not valuation comparisons or coverage lists.
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 access is possible (keyless for public reports, API key for cached library) and what it never delivers (fair value, target, rating). However, it does not explicitly instruct when to use this tool over alternatives or exclude specific scenarios, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_coverageCoverage listAThe honest inventory of what boothcheck currently solves: every US ticker with a live implied-expectations record, with the resolution level each was solved at (whole-company, segment, financials, REIT, and kin). Paginated at 100.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number, 1-based (100 tickers per page) |
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.
most_stretchedMost stretched betsARank US stocks by how much growth is already baked into their price (implied operating-income growth from a reverse-DCF read of the current price). Returns the top N with the growth and duration each price assumes (plus any held margin input). Not a sell list — a ranking of where the bar to clear is highest.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many names to return (1-20, default 8) |
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.
whats_priced_inWhat's priced into a stockAInvert a US stock's current price into the bet it implies: the operating-income growth rate, how many years it must be sustained, and (where the route holds one as an input) the terminal operating margin assumption, versus what the company has actually delivered (from SEC EDGAR filings, reverse-DCF). boothcheck does not return a Boothcheck house fair value, price target, or buy/sell rating — it shows what the price assumes so the user can judge the bet. Covers about 2,000 US tickers.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US stock ticker symbol, e.g. NVDA |
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.
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