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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does communicate the core behavior (side-by-side comparison across specified fields) and implicitly signals a read-only operation warn. But it does not state what happens with invalid symbols, data freshness, or whether the comparison reflects current market prices or historical data. For a read-only comparator this is adequate but not rich.

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

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

A single, front-loaded sentence that immediately states the action and scope, followed by a concise list of attributes. There is zero waste and every word carries meaning. The structure appropriately puts the main verb and object first.

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

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

The tool is low-complexity with one fully documented parameter. The description names the compared fields hearth and the side-by-side intent, which is enough for an agent to invoke it correctly. The lack of an output schema is mitigated by the explicit field list, though it does not specify the output format or error behavior – a minor gap given the 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% – the single 'symbols' parameter is fully described with type, min/max, and an example. The description adds only the range '2–5' which is already in the schema, so it adds no extra meaning beyond what the schema provides. 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 states a specific verb ('Compare'), a resource ('ETFs'), and a scope ('2–5'), and lists the exact attributes compared (price, expense ratio, AUM, YTD return, category). This clearly distinguishes it from sibling tools that fetch single-ETF data (get_etf_quote, get_etf_summary) or holdings/performance, without needing to open schemas.

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

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

The description implies usage when comparing multiple ETFs, and the 2–5 count makes it obvious it is not for a single ETF. However, it does not explicitly mention when not to use it or point to sibling alternatives (e.g., 'for a single ETF use get_etf_quote'). Guidance is contextually clear but lacks explicit routing.

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

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

With no annotations, the description carries the behavioral burden. It discloses the exact data source, update cadence and lag, the fact that positions come from official filings rather than a third-party feed, and the required credential setup. It does not cover failure modes or rate limits, but for a read-oriented data tool the disclosed behavior is substantial.

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 tight sentences: a scoped statement of what the tool returns, a compact list of derived metrics, and two critical caveats (freshness and authentication). Every sentence earns its place, with the most identifying detail 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 description is quite complete for a simple two-parameter read tool with no output schema: it names the source, the input required, the output contents, data staleness, and authentication. The only minor gap is the slight ambiguity between 'every position' and 'top positions,' which the top parameter and context help resolve.

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?

Input schema coverage is 100%, so symbol and top are already documented, including the default and range for top. The description adds context that the listed positions are weighted and that concentration metrics are included, but it does not materially extend the parameter semantics 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 clearly identifies the operation: retrieving every ETF position from the latest SEC Form N-PORT filing, and enumerates what is returned (top positions by weight, concentration metrics, breakdowns). It distinguishes itself mainly by source ('not Yahoo') and 'full' holdings, but it does not explicitly name or contrast the sibling get_etf_holdings, so sibling differentiation is only implicit.

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 useful context on when the data is appropriate: quarterly and about 60 days behind, sourced from SEC N-PORT rather than Yahoo. It also states the SEC_USER_AGENT prerequisite. However, it never explicitly says when to choose this tool over get_etf_holdings or other ETF siblings, so usage guidance is implied 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?

No annotations are present, so the description carries the burden. It discloses the data source (Yahoo Finance), the selection limit (top 10), and the output concept (portfolio weights), which conveys the read-only nature implicitly. It does not mention rate limits, error behavior, or freshness, but for a simple holdings lookup the core behavior is clear.

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

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

Two short sentences: the first states the main function and scope, the second routes to the full-holdings sibling. 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?

For a one-parameter tool with no output schema, the description explains what is returned (top holdings and weights) and how to get the more complete version. It is slightly light on return shape, but 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?

The schema already documents the only parameter (symbol) with 100% coverage, including an example. The description adds no additional parameter-level details, so the baseline 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 names the specific resource (top 10 ETF holdings), the data being returned (portfolio weights), and the source (Yahoo Finance), and explicitly contrasts with get_etf_full_holdings. An agent can immediately distinguish this tool from its siblings.

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 states what the tool covers ('top 10 holdings') and gives an explicit alternative with its differentiator ('for every holding, straight from the fund's SEC filing, use get_etf_full_holdings'). This tells the agent exactly when to choose this tool versus the sibling.

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

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

With no annotations, the description carries the full burden and does well: it reveals the data source, the exact nature of the output, and the dependency that SEC_USER_AGENT must be set to a name and email. It does not mention rate limits or potential failures, but the disclosed auth and data-source context is substantial.

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

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

Three sentences, each earning its place: the core result, the concrete use case, and the required environment variable. The most important information is front-loaded and there is no 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?

For a single-parameter tool with no output schema, the description is complete: it states what the tool returns, why someone would use it, and what setup it needs. An agent has enough to select and invoke it correctly without additional inference.

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%: the schema already documents symbols as exactly two ETF tickers with an example. The description adds no meaning beyond restating that two ETFs are involved, 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 computes how much two ETFs hold the same things, names the data source (full SEC N-PORT holdings), and enumerates the outputs (weighted overlap, shared count, biggest shared positions). This distinguishes it from the sibling tools like get_etf_holdings or compare_etfs and makes the resource and verb 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?

"Use it to check whether owning both actually diversifies" provides a clear, concrete context for when to call this tool. It does not explicitly name alternative tools or give when-not-to-use exclusions, so it stops one step short of full 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?

No annotations are provided, so the description carries the full burden. It states it retrieves historical performance but does not disclose whether it is a read-only operation or any potential side effects (unlikely). It also does not mention if the tool returns absolute or percentage returns, or if it includes dividends. Since it's clearly a read-only data fetch, the lack of disclosure is minor but could be improved.

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, clear sentence with no fluff. It efficiently communicates the core purpose and lists the accepted period values. It is appropriately brief and front-loaded with the main action.

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 tool with only two parameters and no output schema, the description is adequate. It covers what the tool does and the inputs. However, it lacks information on what the output looks like (e.g., a chart, a list of numbers) and what the default period is (though the schema states defaults), which could be clarified. Given the simplicity, it meets the minimum viable standard.

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 100% coverage for both parameters, describing each adequately. The description does not add extra meaning beyond the schema, such as the exact format of the symbol or the default behavior of the period. The schema already notes that period defaults to 1y, so the description adds little 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 clearly states the tool's purpose: retrieving historical price performance for an ETF over specified periods. The verb 'Get' and resource 'historical price performance' are specific. However, it does not explicitly distinguish it from siblings like get_etf_quote or get_etf_summary, though the focus on performance periods is fairly unique.

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 mentions the 'given period' and lists valid periods, but there is no explicit guidance on when to use this versus alternatives. The description implies it is for performance data, but does not mention when not to use it or suggest 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?

With no annotations, the description carries the full burden of behavioral disclosure. It states what the tool returns (price, change, volume, key stats) and uses 'current' to imply real-time data, but it does not mention data delay, market hours, or limitations. This is adequate for a simple read-only quote tool but not rich.

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

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

The description is a single, front-loaded sentence with no filler. Every word contributes to understanding the tool's purpose and return fields, and the examples make the input concrete.

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

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

For a one-parameter tool with no output schema, the description covers the key return values and provides clear examples. It is slightly vague about what 'key stats' includes, but overall it is sufficient for an agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds 'any ETF ticker' and additional examples (VOO, VUG), but these are marginal enhancements over the schema's existing 'ETF ticker symbol (e.g. QQQM)'.

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 action ('Get') and resource ('current price, change, volume, and key stats for any ETF ticker'), making the tool's purpose obvious. It does not explicitly differentiate from siblings like get_etf_summary or get_etf_performance, though the focus on current quote data provides some distinction.

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 guidance on when to use this tool versus alternatives such as get_etf_summary, get_etf_performance, or compare_etfs. The description implies it is for current quotes, but it does not state exclusions or selection criteria.

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

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

No annotations are provided, so the description carries the full behavioral burden. It lists output fields but does not disclose data freshness, whether this is a read-only lookup, response shape, or any limitations. The agent can infer it is a read operation from the name, but the description adds little beyond that.

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 sentence that fronts the action and resource, then efficiently enumerates the returned fields. Every word earns its place, and there is no redundant filler.

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

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

This is a simple one-parameter tool with no output schema, so the field enumeration in the description meaningfully conveys what the agent should expect. It is adequate for invocation, though it would be stronger with a note distinguishing it from quote or performance 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 schema already fully documents the single parameter 'symbol' with an example ('VUG'), so schema coverage is 100%. The description adds no additional semantic detail about the parameter, but the baseline of 3 is appropriate because 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 specific action ('Get detailed fund info') and resource (ETF), and enumerates the exact fields returned: expense ratio, category, AUM, NAV, fund family, and inception date. This is clear, but it does not explicitly distinguish itself from siblings like get_etf_quote or get_etf_performance, so it misses the top score.

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 guidance about when to use this tool versus the sibling tools. The sibling names imply alternatives, but the description never states that this is the best choice for static fund profile data rather than quotes, holdings, or performance, leaving the agent to guess.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (Yahoo Finance's recommendation engine) but doesn't explicitly state it's a read-only operation or describe output format or potential limitations. The implication of 'find' suggests read-only, but that's not stated outright.

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, concise sentence that front-loads the primary action ('Find ETFs similar') and adds a brief purpose ('Useful for discovering alternatives'). No unnecessary words or details.

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 the core function and context. It doesn't specify the return format (e.g., list of tickers) or any pagination, but these are minor gaps for such a straightforward lookup. The absence of annotations is partially compensated by the clarity of purpose.

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%, and the parameter 'symbol' is well-documented in the schema with an example. The tool description doesn't add parameter-specific details beyond what the schema already provides, 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 finds ETFs similar to a given ticker, using the verb 'find' and the resource 'similar ETFs'. This is distinct from siblings like get_etf_quote or get_etf_summary, which focus on other aspects.

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

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

It provides clear context by saying it's 'useful for discovering alternatives', which implies when to use it. However, it doesn't explicitly contrast with sibling tools like compare_etfs or mention when not to use it, so it lacks exclusions.

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

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

No annotations are provided, so the description carries the behavioral burden. 'Search' inherently signals a read-only operation, and the description discloses what the caller can expect in return: matching tickers and fund names. It does not detail sort order or matching semantics, but those are secondary for this tool.

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

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

Two sentences with no filler. The primary action and scope are front-loaded, examples are compactly parenthesized, and the return output is stated directly. 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 simple two-parameter search tool, the description is sufficiently complete: it covers the input intent, representative queries, and the output content. The absence of an output schema is mitigated by the explicit statement that matching tickers and fund names are returned.

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 schema already documents both `query` and `limit` including defaults and max. The description adds helpful examples like 'AI' and 'clean energy', but it does not materially expand beyond what the schema already provides, so the 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 a specific verb and resource: 'Search for ETFs by keyword or theme'. It also gives concrete examples and explicitly states the return value ('matching tickers and fund names'), which distinguishes it from siblings that retrieve quotes, summaries, or holdings.

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 for when to use the tool: when searching for ETFs by keyword or theme. It does not explicitly name alternatives or state exclusions, but the context is unambiguous and the sibling tool names help reinforce the boundary.

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