Tickwind US Market Boards

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

The description complements the readOnlyHint annotation by providing additional behavioral clarity: it states the data source (company filings) and clarifies that the buyback figure is cash spent, not net of new issuance. This helps users understand the computation, though it does not detail any potential side effects (which are none given read-only).

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, using a few sentences to convey the tool's purpose, both views, data source, and a critical clarification. There is no redundant information; each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers essential aspects: what it does, the parameter usage, and data nuances. It does not describe the output format, but given no output schema is provided, this is acceptable. The description is nearly complete for its scope.

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 only provides 'view' with an enum and a brief description. The tool's description expands on each enum value (cannibals = largest repurchase as share of market cap; diluters = fastest share-count growth), giving full meaning. This significantly enhances understanding 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: ranking US companies by share-count changes (buybacks vs. dilution). It explicitly defines the two views (cannibals and diluters) with specific criteria. This is distinct from sibling tools like congress_trades or dca_rating, which focus on different financial topics.

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 effectively explains how to use the required 'view' parameter by detailing what each enum value returns. However, it does not explicitly contrast with alternative tools or provide context on when to choose this over others, though the distinct topic makes this less critical.

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

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

Beyond the readOnlyHint annotation, the description discloses important behavioral traits: the data comes from periodic reports, the newest disclosure is not the newest trade, and dollar amounts are only given as ranges. These caveats inform users about data limitations and reliability, adding significant value.

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 three concise sentences that front-load the primary purpose, then add essential source context and caveats. Every sentence contributes necessary information without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description provides comprehensive context: the exact data source, the 45-day filing delay, the range-based dollar amounts, and the sorting order. It fully covers what a user needs to know to correctly interpret results.

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, and the schema coverage is 100% (empty schema). The description does not need to explain parameters, so the baseline of 4 is appropriate. The description adds no parameter details but none are needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists recently disclosed stock trades by US Congress members, sorted newest first. It uses a specific verb and resource, and the name 'congress_trades' directly matches the function, distinguishing it from sibling tools focused on other 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 provides clear context about the data source and a critical caveat about the 45-day filing delay, helping the user understand when the data is most relevant. It does not explicitly mention alternative tools or when not to use this tool, but the context is sufficient for the tool's straightforward purpose.

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 annotation already declares readOnlyHint=true, and the description builds on that by explaining the computation inputs (filings and daily bars) and framing the output as structural only, not advice. It does not cover update frequency or exact output format, but for a read-only tool this is adequate.

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 focused sentences with no filler. The core purpose is front-loaded, and the caveats are placed clearly at the end.

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, read-only tool with no output schema, the description covers the purpose, input, output scale, computation basis, and disclaimers. This is sufficient for an agent to select and 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?

The schema provides full coverage for the single ticker parameter, so the description does not need to restate it. The description adds the US-stock context and DCA focus, but no additional parameter-format or syntax detail 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 a specific verb ('read') and resource ('one US stock'), explaining that it produces a rules-based 0-100 score for dollar-cost averaging suitability. This distinguishes it from sibling tools such as buyback_leaders or risk_filings.

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 when to use it—for assessing how well a stock fits scheduled recurring buying—and adds exclusions by stating it is not a recommendation or investment advice. It does not explicitly name alternatives or say when not to use it, but the context is clear.

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

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

Annotations only provide readOnlyHint: true, and the description adds no further behavioral details such as data freshness, latency, or error conditions. The description focuses entirely on the query scope, not on runtime behavior or side effects.

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—two sentences—and front-loads the core purpose. It groups related filters (source, exclusions, market-cap band) without redundant wording, making it easy 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?

Given the simple tool (no parameters, no output schema), the description fully covers the input and output context: it specifies the universe (US small caps), the criteria (multiple distinct insiders, open-market buys, last 30 days), ranking logic, and source. No additional context is needed.

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 coverage is complete. The description provides no additional parameter-related meaning, but this is acceptable given the absence of parameters. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: it identifies US small-cap stocks with multiple distinct insiders buying on the open market in the last 30 days, ranked by buyer count and dollar value. This distinguishes it from sibling scan tools like buyback_leaders or unusual_options, which target different signals.

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 (e.g., for insider sentiment for large caps or for other insider actions). While the purpose is self-evident, no direct guidance on applicability or exclusions beyond the market-cap band is provided.

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

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

The annotation only provides readOnlyHint=true; the description adds meaningful behavioral caveats: data is as of a quarter-end, filed up to 45 days later, long-only US equities, and never contains shorts, bonds, or non-US holdings. This is valuable transparency beyond the annotation and contradicts nothing.

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 purpose, and each clause adds essential information about scope, source, timeliness, and exclusions. There is no fluff or repetition.

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 tool with no output schema, the description is complete: it explains what the result contains, what data source it is based on, the reporting lag, and the major coverage gaps. Sufficient for selecting and 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?

The tool has no parameters and the schema is empty, so the baseline is 4. The description compensates by explaining what data is returned and the coverage limitations, even though there are no parameter-level details needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reports which US stocks tracked superinvestor funds added or opened, plus how many were trimmed or exited. The SEC 13F source and limitation to long US equities help distinguish it from sibling insider/congress/buyback 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 gives clear context and useful exclusion guidance: these are historical positions, not current trades, and shorts, bonds, and non-US holdings never appear. It does not explicitly name alternative tools, but the when-not-to-use conditions are strong enough to guide selection.

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

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

Beyond the readOnlyHint annotation, the description discloses the methodology (clustering from daily bars, swing highs/lows, volume shelves), the nature of output (nearest bands with distances), and a significant limitation (never forecasts). This gives a thorough understanding of the tool's behavior and non-behavior.

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 efficiently written with no redundant words. Each sentence adds value: it defines the output, the method, and the limitation. It is well-structured and information-dense without being verbose.

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 (single parameter, no output schema), the description is complete. It explains what the tool does, how it works, what it returns, and what it does not do. There is no ambiguity about the tool's purpose or output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the only parameter ('ticker' as a US exchange symbol with example). The tool description adds no additional semantics about the parameter, but since schema coverage is 100% (one parameter fully described), the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly explains that the tool provides historical support and resistance levels for a single US stock, derived from clustering daily bars, swing highs/lows, and volume shelves. It specifies the output includes nearest levels above/below the latest close with distances, and explicitly states it does not forecast. This is specific and distinguishes it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for historical price level analysis and explicitly states it never forecasts, which guides when not to use it. However, it does not explicitly compare to alternatives or state 'use this when you need historical levels.' The 'never forecasts' is a useful usage constraint, but a direct statement about when to employ it would make it fully explicit.

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

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

Annotations already mark the tool as read-only, and the description adds meaningful behavioral context: a mention is not a finding, and the phrase matcher also catches negations like 'no material weakness.' This warns the agent about a real false-positive class.

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 tight and front-loaded: function, source, caveat, and supported values in three short sentences. Every sentence adds value with no filler or redundancy against the schema.

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 one-parameter, read-only tool with full schema coverage and no output schema, the description is complete. It covers purpose, source, allowed values, and a key limitation. No critical missing context is apparent.

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 covers the single 'kind' parameter fully with an enum and description. The tool description adds semantics by explaining that these values correspond to risk phrases and that results are based on text mentions, including negation cases, which helps the agent choose correctly.

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 SEC filings whose text mentions a specific risk phrase via EDGAR's full-text index. This is distinct from sibling tools like buyback_leaders and congress_trades, so purpose and resource are 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 gives clear context for when to use the tool: screening SEC filings for risk-phrase mentions, with the supported values enumerated. It does not explicitly discuss when not to use it or name alternatives, but the use case is obvious from the description and sibling 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?

Annotations already declare readOnlyHint: true and title. The description adds valuable behavioral context: the data source (Cboe), approximate 15-minute delay, and that it ranks within a fixed scan set rather than the whole market. This goes beyond the annotation without contradicting it.

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 covering purpose, source, scope, and key metric. No filler or repetition, ideal length for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters and a read-only annotation, the description provides sufficient context: source (Cboe), delay (~15 min), scope (most-traded, not whole market), and key metric. It doesn't describe the output format, but for a zero-parameter read-only tool with an output schema absent, this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist (schema is empty), so there is nothing to explain. The baseline for zero parameters is 4. The description clarifies the output (most-traded contracts with ratio) but does not need 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 clearly states the tool's purpose: to list today's most-traded US single-stock option contracts by volume, including volume-to-open-interest ratio. It distinguishes from sibling tools (which cover corporate actions, insider trades, etc.) by explicitly mentioning options and the specific 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 provides context (source, delay, fixed scan set) but does not explicitly state when to use this tool versus alternatives or when not to use it. It implicitly implies this is for options trading activity, but lacks explicit guidance on selection criteria or exclusions.

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