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

No annotations are present, so the description carries the behavioral disclosure burden. It adds useful context about non-December fiscal year ends being handled automatically and about returning actual reported values, but it does not mention read-only status, error behavior, or data limitations.

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 purpose is front-loaded, and the Args/metric list is well-organized. The metric list is long but earns its place because XBRL concept names are not obvious and the schema provides no descriptions.

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 0% schema coverage and no annotations, the description is nearly complete: all four parameters are explained and the metric dictionary removes guesswork. It does not specify units/currency or invalid-ticker behavior, but the output schema covers return structure.

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 0%, so the description fully compensates: it explains tickers format, maps XBRL concept names to human-readable metrics, clarifies year default behavior, and defines quarter semantics. This goes well beyond the bare schema.

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?

States a specific verb ('Compare'), a specific resource ('a financial metric across multiple companies'), and a data source ('SEC XBRL data'). It also clarifies that it returns actual reported values, which helps distinguish it from normalized or adjusted comparison 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 clearly implies the use case: cross-company metric comparison from SEC filings. However, it never explicitly states when to prefer this over siblings like compare_stocks, compare_sectors, or compare_industries, nor does it mention exclusions or 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?

No annotations are present, so the description carries the full burden. It discloses the data source (Finviz groups page) and notes it is a 'current snapshot,' implying no historical data. However, it does not explicitly state read-only behavior, potential rate limits, or any other side effects, though these are not critical for a simple comparison 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 concise and well-structured, leading with the core purpose and then detailing parameters in an Args section. There is no fluff; every sentence adds value, including the pointer to the alternative 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?

With an output schema present, return values are covered elsewhere. The description covers the data source, parameter options, and the alternative tool. It does not mention default sorting behavior beyond the 'order' default, but this is minor. Overall, the description is sufficient for an agent to call the tool 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 description coverage is 0%, so the description must compensate. It explains the 'view' parameter with three enumerated values ('overview', 'valuation', 'performance') and the 'order' parameter with concrete examples ('name', 'marketcap', 'pe', etc.). It also points to compare_sectors for column details, providing actionable guidance beyond the bare schema.

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 all industries and returns aggregate metrics, specifying the resource (industries) and the action (compare). It explicitly differentiates from the sibling screen_industry by directing users there for screening stocks within a specific industry, making the purpose distinct.

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 an explicit alternative: 'To screen stocks within a specific industry, use screen_industry.' This tells the agent when not to use this tool and what to use instead. The description also implies it is for broad industry-level comparison across all industries, giving clear 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data source (Finviz groups page) and that it returns a 'current snapshot', which gives some context about data freshness. However, it does not disclose any potential side effects, error conditions, or limitations beyond the snapshot nature. For a read-only tool, this is adequate but not rich; it lacks details on pagination or response quirks.

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 well-structured and front-loaded with a clear summary. The parameter documentation is detailed but necessary, and the formatting of view options is readable. It is slightly long due to the column lists, but every line earns its place. The order examples are concise and informative.

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 is relatively simple with two optional parameters, and the description covers the purpose, parameters, and data source. An output schema exists, so return values are handled. It does not explicitly mention that all 11 sectors are always returned or address potential edge cases, but for a comparison tool this is likely sufficient. Overall, the description is complete enough for an agent to invoke 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 description coverage is 0%, so the description must compensate. It thoroughly explains both parameters: 'view' lists all three options with the exact column sets included, and 'order' provides concrete examples of valid sort columns. This goes well beyond the schema, which only declares type and default, making it easy for an agent to select appropriate values.

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: comparing all market sectors with aggregate metrics from Finviz. It specifies that it returns sector-level aggregates for all 11 sectors in a single view, which distinguishes it from sibling tools like compare_industries or compare_stocks. The verb 'compare' and resource 'sectors' are explicit.

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 does not explicitly state when to use this tool versus alternatives like compare_industries or compare_stocks. Usage is implied from the tool's name and purpose, but there is no direct guidance on when to choose this over a sibling. It does not mention any exclusions or alternative conditions, so it relies on the agent inferring the appropriate 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?

With no annotations provided, the description carries the full burden. It discloses the data source (Finviz), the delay (~15-20 min), and the point-in-time nature of the data. It does not mention side effects, but a compare operation is inherently read-only and the data source disclosure is the most important 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 front-loaded with the core purpose, followed by data freshness and the key alternative. The args section is compact and informative, and every sentence earns its place without redundant 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?

The description is complete for this tool's complexity: it covers purpose, data source, temporal scope, default behavior, and the alternative for historical comparisons. Since an output schema exists, the description does not need to explain return values, and nothing critical is missing.

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 0%, so the description must compensate. It explains tickers as comma-separated with an example, and explains metrics as optional comma-separated names with a clear default behavior and the full default metric list. It does not enumerate all possible custom metric names, but it provides enough for effective use.

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 states a specific verb and resource: 'Compare fundamental metrics across multiple stocks side-by-side.' It clearly distinguishes itself from compare_financials by emphasizing current snapshot data vs. historical SEC XBRL actuals, so an agent can differentiate between them.

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 explicitly says to use compare_financials for historical comparisons across years, giving a clear when-not-to-use condition. It also clarifies that all values are point-in-time, which guides when this tool is appropriate.

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 behavior. It states the data source (Finviz) and what is shown, but does not clarify whether the operation is read-only (though likely), rate limits, or data freshness. It also doesn't explain what happens with invalid tickers or missing ratings.

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 concise and front-loads the purpose in the first sentence. The args section is clearly laid out and efficient, with no 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?

The tool is simple with 2 parameters and an output schema, so the description covers the essentials. However, it lacks guidance on edge cases (e.g., unknown ticker) and doesn't specify whether the output is ordered or limited, which could matter for agents.

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%, meaning the description must compensate. It describes the 'count' parameter as 'Number of recent ratings to return (default 10)', which is helpful. For 'ticker', it just says 'Stock ticker symbol'—minimal but adequate. No format details like uppercase or exchange suffix 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 it gets analyst price targets and ratings for a stock from Finviz, listing the specific fields (date, analyst firm, rating action, price target). It is distinct from siblings like get_stock_news or get_insider_filings, though it doesn't explicitly name alternatives.

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 mentions it is useful for gauging sell-side sentiment and consensus target price, implying a use case. However, it does not explicitly state when not to use it or name alternatives (e.g., when to use get_stock_news for news sentiment).

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?

Although no annotations exist, the description carries the behavioral burden well: it discloses the data source (Yahoo Finance), that no API key is needed, that prices are split-adjusted, and that results are aggregated per calendar year. It does not detail error handling for invalid tickers or rate limits, but for a free read-only lookup this is a minor gap.

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 compact and front-loaded with the core purpose, followed by aggregation details and data-source context. Minor redundancy exists between 'annual high, low, and average close prices' and the subsequent restatement as 'highest intraday, lowest intraday, and mean daily closing price,' but this is not harmful.

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 read-only tool with an output schema, the description is nearly complete: it explains row granularity, source, split adjustment, and parameter meaning. Nothing an agent needs to invoke it correctly is missing; only edge-case behavior like unavailable tickers is left unspecified.

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%, so the description compensates by defining both parameters. ticker gets examples ('AAPL', 'MSFT'), and years is explained as the number of years of history with its default. It lacks constraints (e.g., positive integer), but the schema's integer type and default cover the basic contract.

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?

Uses a specific verb and resource: fetching annual high, low, and average close prices. The second sentence further specifies the output granularity (one row per calendar year, highest intraday, lowest intraday, mean daily close), which separates it from the fundamental-data tools in the sibling list.

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 does not explicitly compare this tool to alternatives like get_financial_history or get_stock_fundamentals, nor does it state when not to use it. Usage is only implied by the tool's purpose: use when annual price summary data is needed.

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 discloses the filtering behavior (applies keyword filter to the full news feed) and the source (Finviz), which is vital since no annotations are provided. However, it does not explicitly state that this is a read-only operation or mention any potential rate limits or data freshness caveats, but the 'get' verb implies read-only and the filtering detail is sufficient for basic understanding.

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 compact, front-loaded with the core purpose, then usage guidance, then parameter details. Every sentence adds value—no filler or redundancy, making it efficient for an agent to parse.

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 only two parameters and a straightforward filtering operation, the description covers purpose, usage, filtering logic, and parameter semantics fully. The presence of an output schema means the description doesn't need to detail return format, and no critical gaps remain for correct 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 has no descriptions for ticker or count, so the description's 'Args' section fully explains both: ticker as a symbol and count as the number of headlines with a default of 10. This compensates completely for the 0% schema coverage and adds meaning beyond the raw schema.

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 fetches earnings-related headlines for a stock from Finviz, and lists the specific filtering keywords. This distinguishes it from the sibling get_stock_news, which returns all news. The verb 'get' and resource 'earnings-related headlines' are precise and unambiguous.

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?

Explicitly states when to use this tool ('post-earnings analysis' or 'building an earnings update') and when not to, directing users to get_stock_news for general news. This gives an agent clear routing logic without needing to infer from 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?

With no annotations provided, the description carries the behavioral burden. It discloses extraction mechanisms (structured TenK/TenQ objects, markdown slicing for 20-F, full-text fallback), confirms no iXBRL overhead, and explains per-section truncation behavior. It does not mention error conditions or rate limits, but the disclosure is strong.

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 longer than average but densely packed with necessary detail, organized from purpose to form-type behavior to parameter documentation. Every sentence earns its place, and the key purpose is front-loaded.

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 is complex due to multiple form types and section-format variations, but the description covers all relevant scenarios, defaults, aliases, and the prerequisite flow. With an output schema present, the lack of explicit return-value documentation is acceptable.

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 0%, so the description fully compensates by documenting all four parameters in detail. It explains sections formats, aliases, defaults, the meaning of empty strings, and per-parameter truncation. This is far beyond the bare schema.

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 states a specific verb ('Fetch') and resource ('specific narrative sections from the most recent SEC filing'), and immediately differentiates behavior across 10-K/10-Q, 20-F, and other form types. This clearly identifies what the tool does and distinguishes it from siblings like get_sec_filings and get_stock_fundamentals.

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 clear context for when to use the tool across form types and provides an explicit prerequisite: 'Use get_sec_filings first to confirm available items.' It does not fully enumerate alternatives or say when not to use this tool, but the usage context is strong.

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 provided, the description carries the full burden. It discloses that values are 'actual reported values — not estimates or scraped data' and highlights quirks like interim filings possibly including both 3-month and 6-month periods. It also notes that 'all' mixes filing types. These are valuable behavioral insights, though it doesn't cover rate limits, errors, or pagination, which are minor for this data retrieval 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 long but well-structured: a brief intro, then an Args section with bullet points. It is front-loaded with the core purpose and then provides necessary parameter details. Every metric line earns its place, though the metric list could potentially be condensed by referencing a full taxonomy. It is not excessively verbose given the complexity.

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?

An output schema exists, so return values are covered by that. The description thoroughly documents all four parameters and their nuances, including period_type behavior. It lacks details on edge cases like invalid tickers or error handling, but for a historical data retrieval tool with an output schema, it is largely 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 description coverage is 0%, so the description must fully compensate. It does: it explains ticker, lists over 20 common XBRL metrics with human-readable meanings, defines periods with a default, and details four period_type options with their filing sources and use cases. This exceeds what the bare schema provides and is essential for correct usage.

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 purpose: 'Get historical financial data from SEC XBRL filings.' It specifies the resource (SEC XBRL) and distinguishes it from estimates or scraped data, setting it apart from sibling tools like get_financial_ttm or get_financial_snapshot. The verb 'get' and the resource are explicit and unambiguous.

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 when to use different period_type values, with 'annual' recommended for financial modeling and 'quarterly' for recent trend analysis. It also warns that 'all' is not recommended for modeling. However, it does not explicitly name alternative tools or state when to avoid this tool entirely, so it falls short of a 5.

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 the full behavioral disclosure burden. It states the data is actual XBRL-tagged data from SEC filings (not estimates) and mentions the underlying edgartools Financials API. It does not disclose any potential side effects, rate limits, or data freshness limitations, though as a read-only retrieval tool these are less critical.

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 structured with a direct first sentence, then a promotional sentence ('best starting point'), an implementation detail, and an Args section. The promotional and API-powered sentences add moderate noise but the overall length is acceptable.

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 single-parameter input, the presence of an output schema, and a description that covers content and data source, the tool is adequately specified for an agent. It doesn't cover edge cases like invalid tickers or filing availability, but those are unlikely to be fully specified in any tool 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?

The input schema only provides a 'Ticker' title with no property description (0% coverage). The description adds 'Stock ticker symbol,' which clarifies the parameter type but provides no format examples or exchange details. For a single simple parameter, this is minimally sufficient.

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 states a specific verb ('Get') and a specific resource ('complete financial snapshot from the latest SEC filing') and enumerates the content (income statement, balance sheet, cash flow statement, quick-access metrics). It also clarifies the data source as actual XBRL-tagged data, not estimates, which helps distinguish it from estimation-based tools. Although it doesn't name sibling tools explicitly, the description itself is unambiguous.

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 a clear usage context: 'This is the best starting point for company financial analysis.' This tells an agent when to choose it. However, it doesn't mention when not to use it or name alternative tools for more specific historical or TTM data, so it lacks explicit 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?

The description discloses key behavioral traits: it sums four most recent quarterly filings for income statement/cash flow metrics, and returns the latest quarter for balance sheet items. It also lists the exact metric names accepted. Since no annotations are provided, the description carries the full burden, and it does a good job explaining the calculation logic. However, it doesn't mention potential edge cases like missing quarters, fiscal year alignment, or data availability for delisted companies.

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 well-structured with a clear opening sentence, a brief explanation of the calculation method, and a formatted list of metrics. It is somewhat long due to the metric list, but every line serves a purpose. The front-loading is good: the core purpose and calculation logic appear before the detailed metric enumeration.

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 complete for a tool with 2 parameters and an output schema. It covers the input format, the metric options, and the calculation behavior. The output schema exists, so return values don't need explanation. Minor gaps: no mention of error handling for invalid tickers, no mention of data frequency or timezone, and no explicit statement about what happens if a company has fewer than four quarters of data.

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 0%, so the description must compensate. It does: it explains the 'tickers' parameter format ('Comma-separated ticker symbols, e.g. "AAPL,MSFT,GOOGL"') and provides a comprehensive list of valid 'metric' values with human-readable labels. The default value for 'metric' (Revenues) is not explicitly stated in the description, but the schema provides it. The description adds significant meaning beyond the bare schema.

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 purpose: 'Get Trailing Twelve Months (TTM) value for one or more companies.' It specifies the verb (get), the resource (TTM financial metrics), and the scope (one or more companies). It also distinguishes itself from related tools like get_financial_history and get_financial_snapshot by explaining the TTM calculation method (summing four quarters vs latest quarter for balance sheet).

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 explains when to use this tool: 'Useful for up-to-date analysis without waiting for the next annual filing.' It also provides a clear list of supported metrics and distinguishes between income statement/cash flow metrics (summed) and balance sheet metrics (latest quarter). However, it does not explicitly state when NOT to use it or name alternative tools for different use cases, such as get_financial_history for historical multi-year data.

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 provided, the description carries the full burden. It discloses important behavioral details: JSON payload is omitted by default and can be appended with include_json, and it specifies conventions for EPS and LTM EPS sources. However, it does not explicitly state read-only nature, rate limits, or potential side effects. While the retrieval context implies read-only, the lack of explicit statement and any error behavior leaves some gaps.

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 organized with a clear lead sentence, a bullet list of covered items, and a conventions section. The additional details are purposeful and not redundant. It is slightly long but front-loaded with the primary action and scope. Every sentence contributes to understanding what the tool does and how to use it.

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 has an output schema (so return format is covered externally) and no annotations, the description provides enough operational detail: parameter explanations, default behaviors, and conventions. It adequately prepares an agent to call the tool correctly. The only significant omission is explicit usage guidance versus alternatives, but that is covered under usage guidelines and does not impact the call itself.

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 0%, so the description must compensate. It does: each parameter is explained in the Args section beyond the schema's titles and defaults. For instance, it clarifies price_years as 'years for annual price history', fundamentals_years as 'annual periods for per-share fundamentals', and notes peg_basis supports only 'industry'. This adds significant value beyond the schema.

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 retrieves a structured extraction package for the valuation workbook INPUTS tab. It enumerates the specific data included (company snapshot, EPS, price history, etc.), which makes its purpose concrete and distinguishes it from sibling tools that focus on narrower data points. This is not a tautology; it names a specific resource and scope.

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 explains what the tool consolidates, implying it is a one-stop shop for the INPUTS tab. However, it does not explicitly state when to prefer this over alternatives like get_stock_fundamentals or get_financial_snapshot, nor does it mention when not to use it. There is no direct comparison to siblings, so the guidance is implicit rather than 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the data source (Finviz), the type of data returned, and the fields shown, which gives a reasonable picture of the tool's behavior. However, it does not disclose whether the data is delayed, whether it covers only recent activity vs. full history, or any rate limits or pagination behavior. For a read-only data retrieval tool, this is adequate but not rich.

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 concise and front-loaded with the core purpose, followed by the data fields and a use case. The Args section is minimal but appropriate for a single parameter. It earns a 4 rather than 5 because the use-case sentence and the sibling distinction could be slightly more integrated, but 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 a single parameter, an output schema, and a clear sibling distinction. The description covers the data source, the fields returned, and the use case. It does not mention data freshness, pagination, or any limitations, but for a simple single-ticker lookup with an output schema, the description is largely complete. A 4 is appropriate because the missing behavioral details (e.g., how far back 'recent' goes) could matter to an agent.

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 0%, so the description must compensate. It does explain the single parameter 'ticker' as 'Stock ticker symbol' in the Args section, which adds minimal meaning beyond the schema's 'Ticker' title. Since there is only one parameter and it is a standard ticker symbol, the description is sufficient but not detailed (e.g., no format examples like 'AAPL' or 'MSFT').

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 retrieves recent insider trading activity (buys/sells) from Finviz, and lists the specific data fields returned (insider name, relationship, transaction type, date, share count, dollar value). It also distinguishes itself from the sibling get_insider_filings by noting that tool covers SEC Form 3/4/5 filings with EDGAR links.

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 a clear use case ('useful for gauging management conviction') and explicitly names the alternative for SEC filings (get_insider_filings). It does not state when not to use this tool beyond the SEC filings distinction, but the context is sufficient for an agent to choose between the two insider-related tools.

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 present, so the description must carry the behavioral burden. It usefully discloses the output contents and data source ('Powered by edgartools' Form4 parser'), but it does not explicitly state read-only/side-effect behavior, authentication needs, or pagination/limitations; for a get tool this is adequate but not complete.

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 compact and well-structured: purpose, output details, alternative usage, then parameter definitions. Every sentence earns its place and the key purpose is front-loaded.

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 read tool with an output schema, the description covers the essential context: what filing types are returned, what data fields are included, the underlying parser, and the sibling alternative for human-readable data. It does not mention edge cases or limits, but these are less critical given the simple parameters and 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?

Schema description coverage is 0%, so the description must compensate for the bare property titles. The Args section adds functional meaning: ticker is a 'Stock ticker symbol' and max_results is the 'Number of filings to return.' This is sufficient for both parameters, though not deeply detailed.

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 verb and resource: 'Get insider trading SEC filings (Form 3, 4, 5) with structured data.' It names the exact form types and the structured-data nature, which distinguishes it from sibling tools like get_sec_filings and get_filing_text.

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 explicitly identifies an alternative: 'For a human-readable summary of recent buys/sells with dollar values, use get_insider_activity (Finviz) instead.' This gives agents a clear when-to-use/when-not-to-use rule without requiring them to inspect other tools.

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. It discloses the data source (SEC XBRL filings), the computation approach ('computed from actual reported values'), and a clear limitation ('max ~10 years of data'). This is meaningful behavioral context beyond a generic 'Get data' statement.

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 well-structured with a lead purpose sentence, a bulleted metric list, and an args section. The 'replaces paid data services like SimFin' note adds context without bloating the text, and each metric listed is distinct and useful.

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 presence of an output schema, the description need not explain return structure. It thoroughly covers purpose, parameters, data source, and limitations, leaving little for an agent to infer before making a correct call.

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 0%, but the description fully documents both parameters: 'ticker: Stock ticker symbol' and 'periods: Number of annual periods (default 10, max ~10 years of data).' This completely compensates for the schema's lack of property 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?

Description states a specific verb+resource: 'Get historical per-share fundamentals from SEC XBRL filings.' The detailed metric list (Diluted Shares, Book Value per Share, etc.) distinguishes it clearly from sibling tools like get_financial_history or get_stock_fundamentals, which cover broader or different financial metrics.

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 by focusing on historical per-share fundamentals from SEC XBRL data, and notes it 'replaces paid data services like SimFin,' but it never names sibling tools or states when not to use it. An agent must infer when this is preferred over get_financial_history or get_stock_fundamentals.

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 must carry the behavioral disclosure burden. It states the data source (SEC EDGAR via edgartools) and implies a read-only listing operation, but it does not explicitly declare that it has no side effects, nor does it disclose any limitations like pagination or rate limits. The description is adequate but not richly transparent about edge behaviors.

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 structured with a purpose sentence, a usage sentence, and a well-organized Args list. It is longer than minimal due to the form_type explanations, but every sentence adds value and the critical information (purpose and usage) is front-loaded. No fluff is present.

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 3 parameters, no annotations, and no schema descriptions, the description covers everything an agent needs: purpose, usage context, parameter semantics, and data source. Since an output schema exists, return values need not be described. It is fully complete for correct invocation and selection.

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 provides no parameter descriptions (coverage 0%), so the description fully compensates. It explains ticker as a stock ticker symbol, form_type with a list of common values and their meanings (including the default ''), and max_results with its default. This is exemplary parameter documentation.

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 'List recent SEC filings for a company with EDGAR links,' which is a specific verb (List) plus resource (SEC filings) and scope (for a company). It further clarifies the purpose by stating it is used to find filing dates and document URLs, and it distinguishes itself from the sibling get_filing_text by positioning itself as the prerequisite step.

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 explicitly says 'Use this to find filing dates and document URLs before calling get_filing_text,' giving clear when-to-use context. It also details form_type options with their meanings. However, it does not mention when not to use it or alternatives like get_insider_filings, so it lacks explicit exclusions but provides sufficient guidance for typical usage.

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 provided, the description carries the full behavioral burden. It discloses the data source (Finviz), the snapshot nature, and the approximate delay (~15-20 min), which are meaningful behavioral traits. It does not mention rate limits or auth requirements, but for a read-only single-ticker snapshot tool, the key behavioral context is present.

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 well-structured and front-loaded: purpose, return content, source, alternatives, and args. There is minor redundancy in repeating 'Finviz' and 'current snapshot' in the 'Data source' line, but overall every section earns its place and the length is appropriate.

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 one-parameter snapshot tool with an output schema present, the description is complete. It covers what the tool returns, the data source, the delay, the scope of fields, and the key alternative for historical data. Nothing an agent needs to invoke it correctly is missing.

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 0%, so the description must compensate. It does so by documenting the ticker parameter with a clear definition and examples ('AAPL', 'BRK-B'), which adds format and usage nuance beyond the bare schema. The guidance is brief but sufficient for a single required parameter.

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 verb and resource: 'Get comprehensive fundamental data for a stock from Finviz.' It lists concrete coverage areas (valuation, profitability, balance sheet, ownership, technical indicators) and explicitly distinguishes itself from historical data tools by calling itself a current snapshot. This makes it easy to separate from siblings like get_financial_history and get_financial_ttm.

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 clearly states when not to use this tool: 'For historical time-series data use get_financial_history or get_financial_ttm.' It also implies the use case—current snapshot fundamental data—through the delay and coverage wording. It does not explicitly compare against get_financial_snapshot or get_per_share_fundamentals, but the 'comprehensive' and '90+ fields' framing provides enough context for an agent to choose correctly.

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. It discloses the return content (headlines with source, timestamp, URL) which is useful, but it does not mention any side effects, potential errors, rate limits, or confirm it is read-only. Since it's a 'get' operation, read-only is implicit, but the description doesn't explicitly state it or any other behavioral nuances.

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 compact and front-loaded. It starts with the purpose, then the return format, then the alternative, then the Args section. Each sentence serves a distinct function with no redundancy. The structure is easy to scan and parse.

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 an output schema present, return values are covered. The description explains both parameters, the purpose, and the distinction from a sibling. It could mention any constraints on the count (e.g., maximum) or note that it is a live fetch, but these are minor gaps given the overall completeness.

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% – the schema only provides parameter names and types, no descriptions. The description compensates fully by explaining both parameters: 'ticker: Stock ticker symbol' and 'count: Number of headlines to return (default 10)'. This adds clear semantic meaning beyond the schema.

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 states a specific verb (get), resource (recent news headlines for a stock), and source (Finviz), and clarifies what is returned (headlines with source, timestamp, URL). It explicitly differentiates from the sibling get_earnings_news, so an agent can distinguish it without opening schemas.

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 an explicit when-not condition: for earnings-specific headlines, use get_earnings_news. This clearly routes away from the tool in that case. However, it does not explicitly state when to use this tool beyond the general definition, but that is implied by the purpose and the alternative, so it's adequate.

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 the behavioral disclosure burden. It discloses that the tool returns roughly 67 categories with valid code values and provides concrete examples. It does not explicitly state read-only behavior, but the verb 'List' and the lookup purpose make that sufficiently clear.

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?

Three sentences, each earning its place: purpose, usage trigger, and return detail. The most important scoping information is front-loaded, and there is no filler or repetition of schema content.

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 zero-parameter lookup tool with an output schema, the description is complete. It explains what the tool returns, gives representative examples, and tells the agent exactly when to invoke it relative to sibling tools.

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 schema fully covers the parameter space. The description adds useful examples of return values, but no parameter semantics are needed; the baseline for zero-parameter tools is 4.

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 and resource: 'List all available Finviz screener filter categories and their codes.' It clearly differentiates this lookup tool from the screening tools it supports by naming screen_stocks and screen_industry as the downstream consumers.

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 explicitly states when to use this tool: 'Call this whenever you need to look up a valid filter code before calling screen_stocks or screen_industry.' This gives an unambiguous trigger condition and names the relevant 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, what it returns, or any side effects. The only behavioral hint is that it 'runs' a screener, leaving the agent to infer the result shape from the output 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 concise, with two sentences plus a clearly formatted Args section. The primary purpose is front-loaded, and the example is placed in the parameter documentation, avoiding unnecessary verbosity.

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 single-parameter tool with an output schema, the description covers the essential input format and use case. It lacks explicit notes on errors, rate limits, or output behavior, but these are partially covered by the output schema and the simplicity of the 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 schema provides only 'type: string' with no description, so the description must add meaning. It does so by specifying that the URL must be a full Finviz screener URL and gives a concrete example, which gives the agent enough to construct a valid input.

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 states a specific verb 'Run' and resource 'Finviz screener from a URL', and distinguishes itself from manual filter input by noting it replicates a custom screen from the UI. This makes the tool's purpose unambiguous and clearly differentiates it from sibling screen tools like screen_stocks.

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 explicitly states when to use this tool: when the user has built a custom screen in the Finviz UI and wants to replicate it without translating filter codes. It implies that alternatives like screen_stocks are for manual filter entry, providing clear context, though it doesn't name them 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool pre-applies the industry filter, which is a key behavioral trait. However, it does not explicitly state that the operation is read-only or non-destructive, nor does it mention any side effects, rate limits, or error behavior. The output format is not described, though an output schema exists. This is adequate but not exceptional.

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 concise and well-structured. It begins with a clear one-sentence summary, followed by a brief rationale, then a structured 'Args:' block with per-parameter explanations. Every sentence earns its place, with no repetition of schema information. It front-loads the core purpose and immediately provides actionable guidance.

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 provides enough information to call the tool correctly: it details all parameters, gives examples, and points to a companion tool for valid industries. Since an output schema exists, the description does not need to explain return structure. It could mention any limits or edge cases, but for a straightforward screening tool, the coverage is sufficient.

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 description compensates for the absence of schema descriptions by explaining all four parameters clearly. It gives concrete examples for 'industry' (e.g., 'banksregional', 'biotechnology'), clarifies that 'table' is a metric view like 'Valuation', specifies 'additional_filters' as comma-separated Finviz codes, and defines 'max_results' as the maximum number of stocks. This adds substantial meaning beyond the bare schema titles.

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 verb and resource: 'Screen stocks within a specific industry using Finviz.' It distinguishes itself from siblings by explicitly labeling it a 'convenience wrapper around screen_stocks that pre-applies the ind_ filter,' which immediately clarifies its scope. The use case (peer group analysis, sector deep-dives) further anchors its purpose.

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 direct guidance on when to use this tool: it is a wrapper around screen_stocks specifically for industry filtering, which clearly differentiates it from the general screener. It also instructs to use list_filter_options to look up valid industry codes, establishing a prerequisite action. This makes the selection criteria unambiguous.

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 the full behavioral burden and does well by disclosing the ~15-20 min delayed Finviz snapshot data source and that results are a filtered list with columns determined by the table parameter. It also enumerates exact columns per table view. It does not mention rate limits or error behavior, but for a read-only screening tool the key behavioral traits are covered.

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 long but organized into labeled sections (examples, prefixes, table views) and front-loaded with purpose. Some redundancy exists, such as the final 'Returns' line and the repeated instruction to use list_filter_options, but the length is largely justified by the parameter complexity.

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?

All five parameters are documented with examples and defaults, the data source and delay are stated, and an output schema exists so return-value detail is not required. The only gap is sibling-tool selection guidance, which is more a usage-guidelines issue than a completeness issue for invoking the tool 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 0%, so the description must compensate, and it does thoroughly. Filters are explained with value-investing examples and a prefix reference; table lists all six views with their columns; order and signal get examples; max_results gets a default. This goes far beyond the bare schema.

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 verb and resource: 'Screen stocks using Finviz filter codes' and positions itself as the primary tool for finding investment candidates. It clearly separates itself from list_filter_options, but does not explicitly differentiate from sibling screen_value_stocks or screen_from_url, so it stops short of full sibling differentiation.

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 gives actionable context: use list_filter_options to discover filter codes, and choose 'Valuation' for value metrics or 'Financial' for margins/ROE. It does not state when to prefer screen_value_stocks or screen_from_url over this tool, so exclusions are missing.

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. It implies a read-only operation via 'screener' but does not explicitly state it is non-mutating or disclose other behavioral traits like rate limits or auth needs. The mention of 'sensible defaults' adds some behavioral context, but the description could be more explicit about safety.

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 succinct and well-organized: a one-line summary, usage guidance, then an annotated Args list. Every sentence adds value, and the structure makes it easy to scan.

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 having no annotations and 0% schema coverage, the description covers purpose, usage, all parameters, and examples. With an output schema present, return values are covered elsewhere. The description is complete enough for an agent to invoke the tool 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 description coverage is 0%, and the description compensates fully. Each parameter is explained with examples, and the min_market_cap mapping to raw filter codes is especially valuable. This goes far beyond the schema's bare titles.

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 the tool is a 'Quick value stock screener with sensible defaults', specifying the verb and resource. It explicitly contrasts with the sibling screen_stocks, saying it is a 'Convenience wrapper' for 'full control', making the distinction unambiguous.

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?

Provides explicit guidance: 'Convenience wrapper around screen_stocks — use that tool for full control over filters. Useful for fast idea generation.' This tells the agent when to use this tool (quick screens, idea generation) and when to use the alternative (full control), leaving no ambiguity.

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 behavioral disclosure burden. It discloses the data source (Finviz), the temporal nature ('current snapshot'), and the output shape (absolute stock value, industry median, and delta for each metric). It does not mention auth, rate limits, or invalid-ticker behavior, but for a read-only retrieval tool this is reasonably transparent.

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 main purpose, then lists metric categories, output elements, data source, and the single argument. It is compact and every sentence contributes useful information, though the metric-category list adds some length without being essential.

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 one-parameter tool with an output schema, the description covers the use case, metrics, output structure, data source, and ticker argument. It lacks explicit usage-vs-alternatives guidance and caveats about snapshot freshness beyond 'current snapshot', but it is complete enough for an agent to invoke the tool 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 description coverage is 0% and the schema only labels the parameter as 'Ticker'. The description compensates by defining the parameter as a stock ticker symbol and providing concrete examples including 'BRK-B', which signals how to format tickers. This adds real meaning beyond the schema.

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 verb and resource: 'Get a stock's valuation and fundamentals relative to its industry.' It clearly distinguishes the tool from siblings like compare_stocks (peer comparison) and compare_industries (industry aggregates) by emphasizing stock-to-industry comparison.

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 use case is implied clearly: use this when you need a stock's metrics against its industry. However, there is no explicit guidance about when not to use it or which alternative tools to prefer for peer-stock comparison, industry-only aggregates, or screening. The context is present but exclusions and alternatives are left to the agent to infer.

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