TickerInside

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is established. The description adds valuable behavioral context beyond annotations: it clarifies the 'covered' universe limitation and specifies exactly what is measured (e.g., weekly returns, three overlap measures).

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-loads the scope ('Any two covered US ETFs') and then enumerates all outputs in a clear list. Every clause adds information and there is no filler 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?

With no output schema, the description takes on the job of explaining what the tool returns, and it does so thoroughly by itemizing overlap metrics, shared holdings, correlations, and fund characteristics. It could further clarify response formatting or exact behavior for uncovered/unrecognized tickers, but for a read-only comparison tool the coverage is strong.

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 the parameters are already fully documented in the schema. The description confirms both are US ETF tickers but does not add meaningful semantic detail beyond that, matching the baseline expectation.

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 identifies the tool's action (compare), the resource (US ETFs), and the specific outputs (overlap, shared holdings, correlation/covariance, cost/return/risk). The focus on 'US ETFs' and overlap/risk metrics distinguishes it from siblings like compare_stocks and overlap_matrix.

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 phrase 'Any two covered US ETFs' gives clear context for when to use the tool and implicitly scopes it to the available ETF universe. It stops short of explicitly naming alternatives or saying when not to use it, so it misses the top score.

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 this as read-only/idempotent/non-destructive. The description adds useful behavioral context: it covers three years of prices, supports a fund ticker on one side, reports fund weight/rank, and notes the 'covered US ETFs' limitation. No contradiction with 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?

Three dense sentences with no filler. The main deliverable is front-loaded in the first sentence, the ETF special case in the second, and the sibling routing in the third. Every sentence earns its place.

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

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

For a two-parameter read-only tool with no output schema, the description provides a solid account of what will be returned and the one special-case input mode. It could be more explicit about exact output shape, but the listed metrics and ETF behavior cover the agent's invocation needs well.

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 baseline is 3. The description adds meaning beyond the schema by clarifying that ticker 'a' may be an ETF ticker, that only one side can be a fund, and that two ETFs should go to compare_funds. This goes beyond the schema's simple 'ETF ticker also works' note.

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 compares two US stocks or a stock and an ETF, and enumerates the specific metrics returned (market cap, valuation, returns, volatility, beta, correlation, ETF holdings). It also distinguishes itself from compare_funds explicitly.

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 explicit routing guidance: 'For two ETFs use compare_funds' and explains that a fund ticker can be used on one side. It does not enumerate all sibling alternatives, but the key distinction from the closest sibling 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 declare readOnlyHint and idempotentHint, meaning the tool is safe and non-destructive. The description adds that every figure carries the date of the source file, which is valuable behavioral context for understanding data freshness. It also specifies the ordering of holdings (largest first), which helps set expectations.

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-loading the main purpose and then listing the data points. Every sentence adds value; no fluff or redundancy.

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

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

Given the tool's moderate complexity, the schema covers parameters, annotations cover safety, and the description explains the output's scope. There is no output schema, but the description adequately details what the agent can expect, so 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 coverage is 100%, so parameters are documented. The description adds value by specifying that 'holdings' is the count of largest holdings to return, and that 0 means none, which is already in the schema but reinforced. It also clarifies that the output includes weights and other metrics, which is not in the parameter descriptions.

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

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

The description clearly states it returns a single US ETF's holdings with weights, cost, assets, returns, volatility, beta, worst fall, and concentration. The verb 'fund_profile' implies a profile, and the description specifies the resource ('US ETF') and the data included, distinguishing it from siblings that compare or 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 implies the tool is for inspecting a single ETF's detailed profile, as opposed to comparing or listing. It does not explicitly state when not to use it or name alternatives, but the context of 'one US ETF' and the sibling names provide clear 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 mark it read-only, idempotent, and non-destructive. The description adds valuable behavioral context: it covers all US ETFs the server can answer for, discloses data freshness, and explains the two data sources (issuer daily files vs. SEC filings). This goes beyond the annotations and clarifies what the tool actually returns.

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 tight sentences, with the core purpose and output context front-loaded, followed by a direct usage directive. No filler or redundant restatement of the title.

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 no-parameter listing tool, the description fully covers what the list contains, data provenance, and when to invoke it. No output schema exists, but the description supplies enough for an agent to understand the expected result.

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 are no parameters, so the schema imposes no burden. The description focuses on the output and scope instead, which is appropriate for a zero-parameter tool.

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 function: list every US ETF the server can answer for, including data date and holdings source. It clearly distinguishes this as a coverage-listing tool from the sibling comparison and analysis 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?

It gives an explicit call condition: 'Call it when you are not sure a fund is covered.' It does not mention alternatives or when not to use it, but the context is clear enough for an agent to select this tool for coverage checks.

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 idempotent, and the description adds meaningful behavioral details: uncovered funds are listed but excluded from percentages, amounts are optional, and money values appear only when amounts are given. This goes well 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?

Two sentences front-load the core purpose and then add edge-case behavior without any fluff. Every clause contributes useful information, and the structure is easy to parse quickly.

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?

Even without an output schema, the description conveys the return shape: company holdings, percentage and money values, and the funds through which each holding is held. It also flags uncovered funds, leaving little ambiguity for a moderately complex aggregation 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 the baseline is 3. The description adds value by explaining that money amounts are conditional on input amounts and that the output identifies which funds each holding comes through, enriching the schema's parameter descriptions.

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

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

States a specific verb ('look through'), the resource (a list of US ETFs), and the outcome (underlying companies with percentages, money, and source funds). This clearly distinguishes it from sibling tools like compare_funds or stock_exposure.

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 makes the intended use case clear: when you have a portfolio of US ETFs and want aggregated exposure to underlying companies. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to select it appropriately.

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, and non-destructive behavior. The description adds value beyond annotations by disclosing that uncovered tickers are listed and skipped, and that results are ranked by overlap weight. No contradiction with 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?

Two tightly written sentences with no filler. The main function is front-loaded, and the edge-case behavior is added as a concise second sentence. Every word 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 one-parameter, read-only tool with rich annotations, the description covers input bounds, output ordering, and edge-case behavior. It could specify the exact return structure, but the absence of an output schema is mitigated by the clear description of ranking and skipping.

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%, including ticker format, case-insensitivity, and the 2–30 bound. The description adds an important semantic nuance: uncovered tickers are not errors but are listed and skipped, which informs how invalid input is handled.

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: computing pairwise ETF overlap and ranking by weight, with the clear use case of detecting duplicated portfolio holdings. It is informative and distinct from siblings like compare_funds, though it doesn't explicitly name or differentiate itself from any sibling.

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 a clear context for use ('to see which funds in a portfolio duplicate each other') but provides no explicit when-to-use or when-not-to-use guidance, no exclusions, and no alternative tool names. Usage is implied rather than directly specified.

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, covering safety. The description adds valuable behavioral detail: the Euler decomposition summing to 100, the diversification ratio, and the specific matrices returned. It also specifies the time window (up to three years of weekly returns), which is not in 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, well-structured sentence that front-loads the primary purpose and lists all key outputs without any filler. Every element contributes to the agent's understanding.

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 lists all major outputs (volatility, risk shares, diversification ratio, matrices) and the time frame. While there is no output schema, the description sufficiently conveys what the tool returns. It could mention edge cases or limitations, but the schema covers constraints like maxItems and optional amounts.

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

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

The input schema has 100% coverage, with detailed descriptions for both parameters (currency and positions). The description adds no additional parameter semantics beyond what the schema already states, so 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 states the tool's function: it computes portfolio volatility, risk decomposition via Euler, diversification ratio, and correlation/covariance matrices for a list of US ETFs. It is specific enough to distinguish it from sibling tools like overlap_matrix or compare_funds, which serve different analytical purposes.

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 analyzing risk contributions in a portfolio of US ETFs. It provides clear context (portfolio of US ETFs, weekly returns over up to three years) but does not explicitly mention when not to use it or name alternatives. This fits the 'clear context, no exclusions' level.

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 goes beyond the annotations by specifying that results are ordered heaviest first, include the holdings date and source (issuer daily file or SEC filing), and return the largest holders plus the total count. This adds behavioral context about output format and data provenance, which is valuable for an agent deciding whether to rely on the results. The readOnlyHint and idempotentHint are consistent with the described read-only 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 a single, dense sentence that front-loads the core purpose and then adds key details (ordering, holdings date, source, largest holders, total count). It is efficient with no wasted words, though it could be split into two sentences for readability without loss of information.

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 no output schema, the description adequately covers what the agent needs to know: what it returns, the ordering, and the source of the data. It does not mention pagination or error behavior, but these are minor for a read-only tool with a 'top' parameter that likely controls pagination. The description is sufficient for an agent to call 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?

The input schema already documents both parameters (ticker and top) with clear descriptions, achieving 100% schema description coverage. The description adds minimal extra meaning beyond confirming that ticker refers to a single stock; it does not provide format details or examples beyond what the schema offers, so the baseline of 3 applies.

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

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

The description clearly states the tool's function: given one stock, it returns the US ETFs that hold it, along with the weight in each, ordered heaviest first, plus the holdings date and source. It uses a specific verb (returns) and resource (covered US ETFs), and distinguishes itself from siblings like look_through (which likely shows a fund's holdings) by focusing on a stock's exposure across funds.

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 implicitly conveys when to use the tool (when you need to know which ETFs hold a stock and at what weight), but it does not explicitly mention alternatives or conditions for not using it. There is no guidance on choosing between this and sibling tools like compare_funds or look_through, so the agent must infer the appropriate context.

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