Nexqual

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

Annotations already establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful context such as the 4-month lookback window and that price targets are measured against the current price, but it does not disclose other behavioral details like data freshness or market coverage 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 entire description is one well-structured, front-loaded sentence that uses a colon to efficiently list all returned components. There is no filler or redundant restatement of the tool name or annotations.

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 read-only tool with no output schema, the description covers the main return dimensions well: rating, analyst count, price targets, implied upside/downside, and rating breakdown over time. It does not explicitly describe response formatting or currency, but these are not essential for an agent to decide 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 100% for the single 'symbol' parameter with the example 'SOFI', so the schema already carries the semantic weight. The description reinforces that the tool targets US stocks but does not add new parameter-level 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 names the exact resource: Wall Street analyst consensus for a US stock, and enumerates the specific data returned (rating, price targets, breakdown). This clearly distinguishes it from siblings like get_superinvestor_consensus or get_stock_quote by specifying 'Wall Street analyst' and the forecast-oriented contents.

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: when the agent needs analyst consensus data for a US stock. However, it provides no explicit guidance about when not to use it or which sibling tools might be better for related needs, such as current price or earnings 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context by specifying the data source (latest SEC 10-K with a filing link) and detailing the health score components (growth, profitability, balance sheet, cash flow, efficiency), which goes beyond what annotations provide.

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 a single but well-structured sentence that front-loads the core purpose ('Company profile for a US stock') before listing details with semicolons. It conveys a rich amount of content without being wordy or redundant, striking a good balance between detail and efficiency.

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

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

For a tool with one parameter and no output schema, the description thoroughly explains the returned content, including the source of the 10-K notes, the filing link, and the health score's five dimensions. It does not specify the output format, but that is not required given the absence of an output schema, and all essential information for calling the tool correctly is present.

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 fully documents the only parameter (symbol) with 'US ticker, e.g. NVDA', giving 100% coverage. The description does not add any extra detail about the symbol parameter, so the baseline score of 3 applies because the schema already handles parameter semantics.

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 returns a company profile for a US stock and enumerates specific data points: sector, industry, headquarters, CEO, website, market cap, 10-K business notes, and a financial health score. This is distinct from sibling tools that focus on analyst consensus, earnings, insider buys, or stock quotes, so an agent can unambiguously identify what this tool does.

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?

While the description does not explicitly name alternative tools or state when not to use it, it clearly implies this tool is for a comprehensive company overview. The context of a full profile with 10-K and health score makes its use case obvious, and no exclusions are needed since it differs sharply from the siblings.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior, so the description does not repeat those. It adds valuable behavioral context: the source of the data, the dual-extraction verification method, and the coverage limitations. No contradiction with the annotations exists.

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 four sentences, and each sentence conveys a distinct, useful piece of information: content, source, verification, and scope/exclusion. It is appropriately sized for the tool's complexity and front-loads the core result definition. There is no filler, though it is slightly dense.

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 read-only tool with two optional parameters and no output schema, the description covers source, coverage, content, and exclusions, offering enough context for an agent to decide whether to call it. It does not explicitly describe the return shape or pagination behavior, but the schema already communicates the limit default and symbol usage.

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 documents symbol in detail and provides constraints for limit, but the tool description does not mention either parameter explicitly. With only 50% schema description coverage, the missing meaning of limit is not compensated for in the description. The coverage notes help interpret symbol values but add little parameter-level semantic value.

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

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

The description names the resource precisely: reported quarterly EPS and revenue drawn from SEC 8-K filings and earnings press releases, with an explicit note that it excludes analyst estimates. This clearly differentiates it from sibling tools like get_analyst_consensus. The inclusion of scope details (US companies, $10B+ market value, date range) further pins down exactly what the tool returns.

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 by stating 'Reported actuals only: no analyst estimates,' which implicitly tells an agent not to use this tool when estimates are needed. It also gives coverage constraints. However, it does not explicitly name the alternative sibling tools or provide an explicit when-to-use/when-not-to-use rule.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavior beyond annotations: it discloses what is excluded (private placements, IPO allocations, DRIP, ESPP), defines selection thresholds, and describes a special output feature (cluster buying flag within 14 days). This goes well beyond the structured annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph of four sentences, each carrying distinct information: core purpose, exclusions, filters, and cluster-buying flag. It is front-loaded with the primary purpose and avoids fluff. It is slightly longer than strictly necessary, but all content earns its place, so it is well-structured and efficient.

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 lack of an output schema, the description does a good job of explaining what the tool returns (recent purchases, cluster-buying flag) and the criteria that shape results. It covers exclusions, thresholds, and all filters except 'limit'. It could be more complete by mentioning the default look-back and limit values or the ordering of results, but these are minor gaps for a read-only list tool with no output schema. Overall, an agent would call this correctly based on the 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 has 80% description coverage, including clear descriptions for days, symbol, min_value_usd, and executives_only. The tool description restates some of these ('look-back window', 'minimum size', 'executives only') but adds little new semantic detail. The 'limit' parameter is not described in the schema and the tool description also doesn't mention it, so that gap remains unresolved. Overall, the description adds minimal 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 names the specific resource ('open-market insider purchases'), the regulatory basis (SEC Form 4, transaction code P), the actor scope (executives, directors, 10% owners), and key thresholds ($1B market value, $250K purchase). It clearly distinguishes this from sibling tools like get_analyst_consensus or get_stock_quote by focusing on insider transaction data. The scope 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?

The description implies when to use the tool (when you need recent open-market insider purchases, especially monitoring cluster buying), but it does not explicitly name alternatives or state when not to use it. The filter hints ('Filter by ticker, look-back window, minimum size or executives only') give usage context, but the routing to this tool versus a sibling is left to the agent's inference.

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 cover readOnly and non-destructive behavior, and the description adds the per-call ticker limit and the full set of returned quote components. It does not mention data delay or formatting, but for a simple lookup the added context 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?

One dense sentence leads with the core purpose, lists the returned fields, and ends with the cardinality limit. There is zero filler and every part earns its place.

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

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

With a single parameter and no output schema, the description compensates by enumerating the return payload and the allowed batch size. An agent has enough context to invoke the tool correctly and to distinguish it from sibling financial-data 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 only parameter, symbols, is fully documented in the schema with examples and constraints. The description's 'US stocks' and 'up to 10' mirror the schema's minItems and maxItems, adding no new semantic information beyond what the schema already provides.

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

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

The description explicitly states it provides the latest quote for one or more US stocks and enumerates the included fields (price, change, day range, volume, 52-week high/low, market cap). This makes it easy to distinguish from siblings like get_company_profile or get_earnings_results.

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 the tool is for current quote data, but it does not explicitly say when to use it versus alternatives or mention any sibling tools. The sibling names suggest other data types, but no clear routing guidance is provided.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful behavioral context beyond that: it confirms the data is from the latest 13F filings, and it explicitly states that full exits are not included in the most_sold view. This clarifies edge cases that an agent might otherwise assume.

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 a single purpose sentence followed by a compact definition of each view value. No filler or duplication; the key scope 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 has no output schema, so the description should hint at the return shape. It conveys that stocks are returned ('stocks held by...'), but it does not specify the response structure (e.g., ticker symbols, counts, percentages). Given the complexity of the view modes and the absence of an output schema, this is a moderate gap.

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?

With only 33% schema description coverage, the description must compensate, and it does for the most complex parameter: the view enum is fully explained with specific meanings for each option. The limit parameter is left to its name/default, and min_portfolio_pct has a schema description. This is strong coverage for the core functional 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 states the exact resource (aggregate view across all tracked superinvestors) and the data source (latest 13F-HR filings), and enumerates the four view modes with their meanings. This clearly differentiates from sibling tools like get_superinvestor_portfolio and get_analyst_consensus.

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 defines what the tool does but does not explicitly contrast it with the sibling tools or state when to prefer it over individual-holder or portfolio tools. The 'aggregate' nature implies usage, but there is no explicit when-not or alternatives, so this is implied usage rather than explicit guidance.

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, idempotentHint, and destructiveHint, so no contradiction exists. The description adds useful behavioral context by specifying the data source ('latest SEC 13F-HR filings'), the limited universe ('tracked superinvestors'), and the exact returned fields including quarter-over-quarter change.

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 one front-loaded sentence that answers what the tool does before elaborating on the return payload. The parenthetical examples clarify the meaning of 'tracked superinvestors' without adding filler, and every clause contributes either scope or return detail.

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 lookup, this description is complete enough. It names the data source, the input domain, the holder universe, and the exact return fields, which matters because there is no output schema. The agent can successfully invoke and interpret the result without further documentation.

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

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

Schema coverage is 100%, so the single symbol parameter is already fully documented with examples like AAPL, GOOGL, and BRK.B. The description reinforces that the input is a US stock, but it adds no new parameter-level semantics 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 the exact query—'Which tracked superinvestors hold a US stock'—and backs it with concrete example names, making the tool's resource unmistakable. It also differentiates itself from siblings like get_superinvestor_portfolio (which would show one investor's holdings) and get_superinvestor_consensus (which would aggregate rather than list per-holder data).

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 context is clear: this tool maps a stock symbol to individual 13F holders and returns per-holder fields. However, it never explicitly says when to prefer this over related sibling tools, such as get_superinvestor_portfolio for the inverse direction or get_superinvestor_consensus for aggregate views. The agent must infer the distinction.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the data source (SEC 13F-HR) and the nature of the output (holdings, changes), but it doesn't disclose potential limitations like 13F filing delays, missing positions, or that the data is quarterly. This is acceptable given the annotations, 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 a single, information-dense sentence that front-loads the core purpose and then lists the key output fields. It includes concrete examples for the parameter, making it immediately actionable. No wasted words.

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

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

For a read-only, idempotent tool with a simple two-parameter schema and no output schema, the description covers the essential information: what it returns, how to specify the investor, and the optional limit. It could mention that 13F data is quarterly or that the tool only covers tracked superinvestors, but these are minor gaps given the tool's simplicity.

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

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

Schema description coverage is 100%, so both parameters (investor and limit) are already documented in the schema. The description adds examples of valid investor values and clarifies that limit controls the number of holdings returned, but this is marginal value beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves the latest SEC 13F-HR portfolio of a tracked superinvestor or fund, with specific details like holdings ranked by portfolio weight, share counts, reported values, and quarter-over-quarter changes. It distinguishes itself from siblings by focusing on a single investor's portfolio rather than consensus or holders lists.

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

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

The description explains what inputs are accepted (investor name, fund name, or Nexqual slug) and gives examples, which helps an agent know how to invoke it. It doesn't explicitly state when to use this tool versus alternatives like get_superinvestor_consensus or get_superinvestor_holders, but the focus on a single portfolio is clear enough to imply the distinction.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so safety and idempotency are covered. The description adds useful context about the data source and output fields, but it does not disclose behavior like default ordering or how the closed tracked universe is maintained. That is not a contradiction, just limited additional behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The first sentence fronts the core behavior and return fields; the second gives a concrete downstream use. Every clause earns its place.

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

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

For a simple read-only list with only two optional parameters and no output schema, the description covers the source, the returned fields, and the intended use. Combined with the annotations and schema, an agent has everything needed 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 for sort and limit semantics, but it does not. It never explains that sort controls ordering by portfolio_value or name, or that limit caps the number of returned investors; the schema only gives enums and defaults, leaving the agent to infer behavior.

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 names a specific action ('List'), a bounded resource (superinvestors and funds Nexqual tracks), and a data source (SEC 13F-HR filings). It also lists the returned fields and ties the tool to a sibling (get_superinvestor_portfolio), so an agent can distinguish it from the other superinvestor 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 explicitly tells the agent to use this tool to discover valid names for get_superinvestor_portfolio, giving a clear use case. It does not enumerate when not to use it or compare against get_superinvestor_holders/consensus, but the stated purpose is sufficient for routing.

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