Nexqual

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false, and destructiveHint=false, so safety and idempotency are covered structurally. The description adds a genuine behavioral constraint beyond the annotations — coverage is limited to US stocks — which warns the agent about possible empty results. It does not disclose data freshness/update cadence or how many analysts contribute, which would be the remaining useful 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?

A single sentence, front-loaded with what the tool returns, with every clause carrying information (rating, targets, implied move, buy/hold/sell distribution, US-only scope). No filler or redundancy.

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

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

With no output schema, the description bears the burden of describing the return shape, and it does so thoroughly by enumerating the target statistics and rating breakdown. Annotations cover the safety profile. Remaining gaps — data freshness and behavior when no analysts cover the symbol — are minor for a single-parameter read 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?

There is only one parameter (symbol) at 100% schema description coverage, so the schema fully documents it; baseline is 3. The description's 'for a US stock' adds a mild constraint hint about what the ticker should identify, but no format examples or validation expectations 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 concrete outputs: consensus rating, 12-month price targets with mean/median/high/low, implied move, and buy/hold/sell counts. No sibling (quote, financials, earnings, insider trades, price history, SEC events, profile, superinvestors) overlaps with this data, so the agent can route unambiguously.

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?

Usage is implied by the data returned — an agent wanting forward-looking analyst sentiment would pick this — and the phrase 'for a US stock' implicitly scopes it to US-listed tickers. However, there is no explicit when-to-use guidance, no stated alternative (e.g., vs. get_stock_quote for current price), and no note on what happens for non-US or uncovered symbols.

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 readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuine provenance value by stating the data is quoted from the latest 10-K and SEC annual reports and that the Nexqual score is a 0-10 scale, but says nothing about freshness, coverage of non-US issuers, or behavior on an invalid ticker.

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?

A single dense sentence that front-loads the core fields (sector, CEO, HQ, market cap) before the more specialized 10-K and supply-chain content. It is efficient, though the semicolon-chained clauses make it slightly run-on.

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

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

With no output schema, the description correctly does the heavy lifting by enumerating the return fields, including the scoring scale and the cited-source nature of the text. The only gap is that it never restates the required input or how failures are surfaced.

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?

Only one parameter exists and schema coverage is 100% ('Ticker'), so the schema carries the burden. The description never mentions the symbol argument, adding no format hints (e.g., exchange suffix) or validation behavior beyond what the schema states.

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

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

The description enumerates exactly what the tool returns (sector, CEO, HQ, market cap, 10-K-derived strategy/risks, supply-chain relationships, Nexqual score), which makes its scope clear against siblings like get_financials or get_earnings_results. It lacks an explicit verb and never names an alternative, but the resource 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?

There is no when-to-use guidance, no prerequisites, and no mention of the sibling tools (get_financials, get_analyst_consensus, superinvestors) that overlap conceptually. The agent must infer from the field list alone that this is the broad qualitative profile tool.

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 read-only/idempotent/non-destructive, so the bar is lower. The description adds useful context annotations don't: data provenance (8-K filings, press releases), timing (at publication), and coverage universe (US, ~$300M+ market value), though it says nothing about row limits or ordering.

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?

Dense but front-loaded: the primary content (EPS, revenue, guidance) leads, with the scope rule last. The parenthetical market-value/date detail is useful but slightly crammed into a single sentence.

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?

No output schema exists, and the description does convey the returned fields (EPS, revenue, stated guidance) plus freshness and coverage limits. Pagination/limit behavior is only covered by the schema, a minor gap for a two-param read 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?

Schema coverage is 100%, so baseline is 3, but the description adds real semantics for symbol beyond 'Optional ticker': omitting it returns the latest across all companies. Limit is left to its schema description, which is adequate.

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+resource (reported EPS/revenue plus guidance from 8-K filings and press releases) and explicitly contrasts with estimates, which separates it from the sibling get_analyst_consensus. An agent can distinguish it without opening the schema.

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?

Gives a concrete usage rule: 'Omit symbol for the latest across all,' and implicitly routes estimate-seekers elsewhere via 'Actuals only, no estimates.' No explicit exclusions or prerequisites beyond that.

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, destructiveHint=false, openWorldHint=false, so the safety profile is fully covered. The description adds valuable context about data sources (SEC 10-K/10-Q), historical depth (15 years annual, 40 quarters), and specific ratios included. It doesn't mention rate limits, time delays, or data currency, but with annotations covering behavior, this is a moderate addition.

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?

Single sentence, front-loaded with the core data types, then details on sources and valuation metrics. No wasted words; it efficiently packs substantial information into one sentence.

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 data retrieval tool with no output schema, the description covers what data is returned (income statement, balance sheet, cash flow, valuation ratios) and sources/limits. It lacks explanation of parameter usage and any output format details, but given the simplicity and annotations, 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 67%, with the 'symbol' and 'limit' parameters having schema descriptions but 'period' lacks one (only enum values). The description does not explain the parameters at all, leaving the 'period' enum semantics to be inferred from the enum values alone. Baseline 3 is appropriate given partial schema coverage, but the description could have added meaning for limit or period.

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 verb+resource: fetching financial statements (income statement, balance sheet, cash flow) from SEC filings and valuation metrics. It distinguishes itself from siblings like get_company_profile or get_analyst_consensus by focusing on raw financial data. However, it doesn't explicitly contrast with siblings or state what it does not do.

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?

Usage is implied: use this when you need financial statements or valuation ratios. There is no explicit when-to-use guidance, no mention of alternatives (e.g., for analyst estimates use get_analyst_consensus), and no exclusions or prerequisites. An agent can infer the use case but isn't told about boundaries.

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 the safety profile (readOnly, idempotent, non-destructive), so the bar is low, yet the description adds rich behavior: inclusion/exclusion rules, dollar thresholds, cluster-buy flagging, Rule 10b5-1 plan flag and adoption date, and the routine/opportunistic insider_type classification with its academic basis.

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?

Front-loaded with the core purpose, then layered detail; every clause is information-dense. It is long and the journal citation is arguably extra, but nothing is wasted 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?

No output schema exists, so the description carries return-value burden; it names key returned fields (insider_type, plan flags, cluster flag). It stops short of describing the full response shape, but is sufficient 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?

Schema coverage is 67%, so some params (e.g. min_value_usd, limit) rely on the description. It discloses the side and days defaults ('buys, last 30 days') and the value thresholds that govern filtering, adding meaning beyond the bare schema fields.

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+resource (insider buys/sells from SEC Form 4) and scopes it precisely to US companies ~$1B+. It clearly distinguishes itself from the financial-data siblings by describing the exact data source and filter logic.

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?

Defines what qualifies as a buy (code P, $250K+, excluding private placements/IPO/ESPP/DRIP) and a sell (code S, $1M+), plus the default (buys, last 30 days). It gives clear context for use but never names or rules out the sibling tools an agent might otherwise pick.

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, destructiveHint=false, openWorldHint=false, so the safety profile is fully covered. The description adds content scope (dividends reinvested, splits included) and the range boundary, but nothing about latency, adjustment conventions, or response size/limits beyond what annotations give.

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?

A single dense sentence, front-loaded with the primary output (closing-price series) before the secondary metrics. Efficient, though the long comma list of metrics borders on a data dump rather than 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?

With no output schema, the description sensibly enumerates what is returned (prices, returns, dividends, splits), which is the key missing structured info. It does not state the default range when 'range' is omitted, a minor gap for an otherwise complete read-only 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?

Schema coverage is 50%: symbol is documented as 'Ticker' and range carries an enum. The description restates the range boundary ('1mo to max') but does not clarify the enum values (ytd, max) or symbol format beyond the schema. Baseline 3 for a mostly schema-documented parameter set.

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 resource (closing-price series for a US stock or ETF) and enumerates the exact analytics returned: price return, total return with dividends reinvested, annualized return/volatility, max drawdown, high/low, dividends and splits. An agent can distinguish this from get_stock_quote (current price) and get_financials (statements) without opening the schema.

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?

Scope ('US stock or ETF') and time span ('1mo to max') imply when this applies, but no explicit when-to-use or when-not-to-use guidance and no named alternatives (e.g., 'for a live price use get_stock_quote'). Usage must be inferred from the sibling list.

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, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral value by stating that 8-K items quote the filing's own text and that ownership entries include holder and percent, but it says nothing about result ordering, limits, or how the days/limit windows affect output.

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?

It is one dense but well front-loaded sentence: the core purpose ('recent SEC filings in plain words') leads, then the parenthetical elaborates the event taxonomy. The long list is informative rather than padding, though it sits at the upper bound of what a single sentence should carry.

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

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

With no output schema, the description carries the burden of describing returns, and it does so thoroughly — event categories, quoted filing text, holder/percent fields, and filing dates. It is nearly complete; the remaining gap is operational detail (defaults, ordering, result volume) that the agent might want before calling.

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% — symbol, days (default 180, max 1095), and limit (default 30) are all documented in the schema. The description adds no parameter-level meaning beyond that, which is the expected baseline when the schema does the heavy lifting.

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 a concrete resource — recent SEC filings for a US company — and enumerates the exact filing/event types returned (8-K categories, 13D/13G stakes, 10-K/10-Q/proxy dates), so the agent knows precisely what comes back. It is clear what the tool does, though it never explicitly distinguishes itself from siblings like get_insider_trades, which is a plausible near-neighbor for the 13D/13G ownership 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 enumeration of covered event types implies usage (call this when you need corporate events, ownership stakes, or filing dates), but there is no explicit 'use this when' statement, no mention of when another tool such as get_insider_trades would be preferable, and no stated prerequisites. Usage is inferable rather than stated.

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 the safety profile (readOnly, non-destructive, not idempotent, non-open-world), so the description's job is to add behavior, which it does: a 10-ticker batch cap and, crucially, that outside regular US hours the returned price is the last close rather than live data. Pointing the agent to follow the `note` field is useful but cryptic since no output schema defines that field.

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 dense sentences with the returned fields front-loaded and the market-hours caveat placed last where it matters. Minor deduction for the trailing 'follow `note`' reference to an undefined field, which costs a clause without resolving anything.

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

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

With no output schema, the description carries the return-value burden and does so by enumerating the fields. It also flags the stale-price behavior off-hours. It omits error behavior for invalid symbols and any unit/currency notes, which is the remaining 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 one parameter and 100% schema coverage the baseline is 3, but the description adds a real constraint not present in the schema: the batch is capped at 10 tickers and only US symbols are valid. It does not clarify symbol formatting (e.g. exchange suffixes), which keeps it from a 5.

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 the concrete resource and the exact fields returned (price, change, day range, volume, 52-week range, market cap) plus the US-equity scope, so it is clearly a point-in-time quote tool rather than a historical or profile tool. It does not explicitly contrast itself with get_price_history or get_company_profile, so it stops short of a 5.

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?

There is no when-to-use guidance and no named alternative among the many siblings (get_price_history, get_company_profile, get_analyst_consensus). The 'up to 10 tickers' limit and US-only scope hint at appropriate use but never say when this tool should be chosen over its neighbors.

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 readOnly, idempotent, non-destructive, so safety is covered. The description adds real behavioral context beyond that, notably that portfolio always lists every new/added/reduced position and that the consensus views aggregate across all investors.

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 dense, well-structured sentences that front-load the resource and then enumerate view semantics via semicolons. Efficient and readable, though the second sentence is fairly packed.

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

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

With no output schema, the description carries return-value burden but adequately conveys the semantics of each view for correct invocation. The structural format of returns per view is only implied, leaving a minor gap for a 7-view 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?

Schema coverage is high (80%), so the baseline is 3, but the description adds meaning by explaining what each view enum value returns and noting the min_portfolio_pct default of 0.5, which the schema does not spell out.

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 resource (latest SEC 13F holdings of ~80 tracked investors) with named examples (Buffett, Ackman, Tepper, Klarman), which is clearly distinct from siblings like get_insider_trades or get_sec_events. It's clear what the tool does, though it doesn't explicitly name a sibling to differentiate from.

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?

Maps each view value to a clear use case (holders = who owns symbol; portfolio = holdings of investor; list = tracked investors; consensus views = aggregated signals), effectively routing the agent to the right mode. It lacks explicit when-not guidance or pointers to sibling tools for overlapping needs.

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