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

No annotations are present, so the description bears the burden. It does disclose behavioral details: 'months: Lookback when start_date is omitted' and 'start_date/end_date override' clarify precedence and parameter semantics. However, it omits output format, error handling, and edge cases (e.g., invalid codes, mismatched periods), leaving some behavioral gaps.

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 leads with a one-sentence purpose, followed by a tight Args list. Every line earns its place; there is no redundancy or filler. The structure is scannable and appropriately sized for the tool's complexity.

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?

All four parameters are semantically explained, and an output schema exists, so return values don't need description. It lacks explicit usage alternatives and edge-case handling, but the provided information is sufficient for correct basic invocation. A perfect score would require more guidance on when to prefer alternatives like get_etf_history or rank_etf_return.

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 has 0% description coverage, but the Args section fully compensates. It explains that codes are 6-digit, months is a lookback when start_date is omitted, and start/end dates are YYYYMMDD overrides with explicit precedence. This adds significant semantic meaning beyond the schema's bare types and defaults.

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 'Compare price returns of specific ETFs over one shared period,' identifying both the action (compare) and the resource (price returns of ETFs). This differentiates it from siblings like get_etf_history (single ETF history) and rank_etf_return (ranking), making its purpose 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?

Usage context is implied through phrases like 'over one shared period' and the argument descriptions (months as lookback, start/end overrides), but no explicit 'use when' guidance or exclusions are provided. The description doesn't contrast with sibling tools, so an agent must infer when this is the preferred choice.

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 provided, the description carries the full burden for behavioral disclosure. 'Show' implies a read-only operation and the purpose is clear, but it does not state side effects, permission requirements, or behavior for invalid inputs. This is adequate for a simple diagnostic tool but lacks depth beyond the obvious read-only implication.

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

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

The description is concise, front-loaded with the core behavior, followed by a usage hint and parameter documentation. Every sentence earns its place, and the Args section is clearly structured. There is no redundant text.

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

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

Given the tool's simplicity (one parameter), the presence of an output schema, and the explicit usage context, the description is fairly complete. It covers the purpose, the scenario, and the parameter. It could add notes on error cases or limitations, but those are not essential for such a straightforward 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?

The input schema has zero description coverage, so the description must compensate. It does so effectively by explaining the parameter as 'The ETF product name' and providing a concrete example ('KODEX 삼성전자채권혼합'), which adds meaning beyond the schema's bare 'name' field. For a single parameter this fully resolves ambiguity.

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: 'Show which keywords drove the classification of an ETF name.' This clearly distinguishes it from siblings like search_etf or list_filters, as it is the only tool that explains classification decisions.

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 a clear context: 'Use this to audit a filter result that looks wrong.' This tells the agent when to invoke the tool, though it does not explicitly mention when not to use it or name alternative tools. That 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It reveals that the tool returns side-by-side metrics (size, liquidity, NAV premium) and that base_date is resolved backwards to a day with data. It does not explicitly state read-only behavior, but the list semantic implies it.

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

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

The description is well-structured: a concise opening statement, an illustrative example that adds context without bloat, and a clean parameter list. Every sentence contributes value and the format is easy to parse.

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

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

With an output schema present, the description does not need to explain return formats. It covers purpose, use cases, and all parameter semantics, and even provides a concrete example. No critical gaps are apparent for a read-only list 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?

Despite 0% schema description coverage, the description's Args section fully documents all four parameters: code_or_name, sort_by (with all allowed values), base_date (including backward resolution), and limit (max peers). This adds significant meaning beyond the bare schema.

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

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

The description clearly states the tool lists every ETF tracking the same underlying index as the given one, using the specific verb 'List' and defining the resource scope. It distinguishes itself from siblings by focusing on peer comparison rather than quotes, history, or ranking.

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 a clear use case ('which of these should I hold?') and an illustrative example comparing ETFs tracking the same index. It implies when to use the tool but does not explicitly name alternatives or exclusions, so it stops 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?

With no annotations, the description carries the burden of behavioral disclosure. It explains important default behavior for start_date, end_date, and months (e.g., 'Defaults to the latest day with data'), which goes beyond the raw schema. It does not explicitly declare read-only status, but the tool's purpose makes that obvious, and the output schema covers return structure.

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

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

The description is concise and well-structured: a one-line purpose followed by a compact argument list. Every sentence adds value, and the main purpose 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?

Given the tool's simplicity, the presence of an output schema, and thorough parameter documentation, the description is complete for an agent to use it effectively. No critical information appears missing, and an example is optional rather than necessary.

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 fully compensates by defining each parameter meaningfully: code as a '6-digit short code,' date formats as YYYYMMDD, and the roles of start_date, end_date, and months with their default relationships. This adds substantial clarity beyond the bare schema.

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

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

The description clearly states the tool provides 'Daily closing-price history for one ETF,' which is a specific data product and scope. It naturally distinguishes from siblings like get_etf_quote (current quote) and compare_etf (comparison), even without naming them.

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?

No guidance is given on when to use this tool versus alternatives. The description does not mention sibling tools, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and the description's first line.

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 provided, the description carries the full burden. It discloses that the tool returns every feed field, computes the NAV premium/discount, returns candidates for ambiguous matches, and resolves base_date backwards to a day with data. These are substantial behavioral details beyond the schema.

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 well-structured: a purpose sentence, a list of return fields, a disambiguation note, and an Args section. It is front-loaded with the main purpose, and each sentence earns its place without 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 that an output schema exists to define return structure, the description covers the key contextual behaviors: ambiguous-name handling, date resolution, and computed metrics. It is complete for a single-quote tool, leaving no major gaps.

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 compensates with an 'Args' section that provides concrete examples for code_or_name ('069500', 'KR7069500007', 'KODEX 200') and explains base_date's backward resolution. This adds essential meaning beyond the bare field titles.

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 opens with 'Full one-day detail for a single ETF, looked up by code, ISIN or name.' This clearly states the tool's specific verb ('get'), resource ('ETF quote'), and scope (single ETF, one-day detail), distinguishing it from siblings like get_etf_history and search_etf.

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 (single-day snapshot, disambiguation via candidates) and gives concrete examples, but it does not explicitly name alternative tools or state when not to use it. This is clear context without explicit exclusions, so it falls short of a 5.

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 portal lag and that 'today' is often empty, which explains the tool's behavior. However, it doesn't explicitly state side effects or error conditions, and doesn't confirm read-only status beyond inference.

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 short, purposeful sentences plus an Args line. It's front-loaded with the main purpose, and every sentence adds value.

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

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

For a one-parameter utility, the description is sufficiently complete: it explains what, why, and when to call it, plus the parameter. The output schema can handle return value documentation. It could briefly mention edge cases like no valid date found, but it's not a significant 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?

The schema provides only a type and default for on_or_before, with no description, so coverage is 0%. The description compensates by specifying the format (YYYYMMDD) and the meaning ('search back from'), plus the default behavior. This fully explains the 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 uses a specific verb 'Resolve' and clearly identifies the resource: the most recent date with ETF data. This distinguishes it from sibling tools that operate on ETFs, quotes, history, etc., making it clear this is a date-resolution utility.

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 instructs to 'Call this before assuming any date is usable,' providing clear timing guidance. It explains the portal lag context, which tells the agent when to use this tool, though it doesn't name alternative tools or state when not to use it.

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 must carry the full burden of behavioral disclosure. It only states that values are listed, but does not describe the return format, whether the list is static or dynamic, or any side effects (none presumably). Minimal transparency.

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 immediately conveys the tool's purpose with no redundant or filler content. It is front-loaded and easy to parse.

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

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

Although the tool is simple and has an output schema, the description lacks explicit context about how this tool relates to sibling tools or when an agent should call it (e.g., before using other ETF tools to know valid filter/sort values). It is minimally adequate but not complete in guiding broader task workflow.

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

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

The tool has zero parameters, so per the rubric the baseline is 4. The schema has no properties, and the description does not need to explain parameters. It adds no parameter information, but none is required.

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

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

The description clearly states the tool lists accepted values for filter and sort arguments. The verb 'List' and resource 'filter and sort arguments' are specific, distinguishing it from sibling tools that operate on ETF data. However, it does not specify which context or endpoint these arguments refer to, leaving minor ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives, nor any mention of prerequisites or situations where it would be necessary. The description simply states what it does without context.

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

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

No annotations are present, so the description carries the full burden. It adds some behavioral context like base_date resolving backwards to a trading day and ordering by most-tracked first. However, it does not explicitly state that the operation is read-only or mention any potential side effects, permissions, or error conditions. Still, the verb 'list' implies a safe read operation.

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 well-structured: a one-sentence purpose, a usage note, and a compact bulleted list of parameters. Every sentence is informative and there is no redundant or filler content.

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 covers the tool's purpose, usage guidance, and all parameters thoroughly. There is an output schema, so return structure is already specified. It could be more complete by mentioning behavior when no indices match or edge cases, but for a list tool with well-documented inputs, this is largely sufficient.

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%, but the description fully compensates by explaining each parameter's meaning: query as substring filter with an example, min_etf_count as a threshold, base_date's format and backward resolution, and limit's ordering. This adds significant value beyond the raw schema.

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

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

The description clearly states the tool's action ('List underlying indices and how many ETFs track each') and its role in the broader system as a 'map of the market' to find exact index_name strings. This specific verb+resource+scope distinguishes it from sibling tools like search_etf and find_similar_etf.

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 explicitly instructs when to use this tool: 'use it to find the exact index_name string that search_etf and find_similar_etf expect.' This provides a clear use case and directs the agent to related tools, fulfilling the 'explicit when' criterion without ambiguity.

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 provided, the description carries the full burden of disclosing behavior. It explains how endpoints are resolved to trading days, how missing prices are reported in `excluded` rather than dropped, and the effect of the `worst` flag. This goes beyond basic expectations and provides significant behavioral insight, though it does not mention aspects like pagination or rate limits.

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 well-structured, starting with a concise purpose statement and then a clean Args list. Each parameter line is compact and informative, with examples where helpful. There is no redundant or irrelevant content; 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?

Given the complexity of 13 optional parameters and the existence of an output schema, the description provides all necessary context: the tool's purpose, filter options, behavioral nuances (missing data handling, worst flag), and parameter semantics. Since the output schema exists, it does not need to detail the return structure, making this description effectively complete for an agent.

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 0% description coverage, but the description compensates by listing all 13 parameters with short, meaningful explanations and examples (e.g., 'query: Name substring filter, e.g. "채권혼합"'). This fully adds semantic value beyond the raw schema properties, which contain no 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 opens with 'Rank ETFs by price return over a period', which clearly states the specific action and resource. This distinguishes it from siblings like compare_etf (which compares specific ETFs) and get_etf_history (which provides historical data). The optional filter set further clarifies the scope.

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 clearly states the tool's use case: ranking ETFs by return with optional filters. It does not explicitly mention when not to use it or name alternatives, but the purpose is unambiguous enough for an agent to select it when a ranked list is needed. The context is clear, though exclusions are not explicitly 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?

With no annotations, the description carries the behavioral burden and does so well: it notes classification is derived from the product name because the upstream feed lacks a category field, and that base_date resolves backwards to a day with data. It also explains exclude behavior and former brand-name resolution, adding context beyond simple parameter lists.

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 front-loaded with a one-sentence purpose, followed by a clear 'Args' block that groups parameter semantics. Despite length, it remains scannable and every line adds necessary detail for a 13-parameter tool; 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?

Given the complexity (0 required params, 13 optional, no annotations) and the presence of an output schema, the description covers all necessary behavioral and parameter-level nuances. It even notes tricky behaviors like base_date backward resolution and former issuer brand names, making it complete for an agent to select and invoke 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 compensates fully by explaining every parameter with examples and nuanced details. For instance, issuer supports former brand names, index_name should be an exact underlying index with a pointer to list_indices, and sort_by enumerates all valid values. This gives the agent far more than the raw schema 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 opens with 'Find ETFs by name fragment, issuer, index and/or classification,' which clearly states the verb (find) and resource (ETFs). It also calls itself 'The main lookup tool,' distinguishing it from sibling tools like get_etf_quote or find_similar_etf.

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 identifies this as the main lookup tool and explains how filters combine with AND, giving a clear usage context. It references list_filters and list_indices for valid values, but does not explicitly contrast with alternative search-related siblings like find_similar_etf.

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 that the tool returns known limitations, indicating transparency about server constraints. However, it does not mention whether it is read-only or if there are any other behavioral caveats, though for an info endpoint this is largely self-evident.

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 directly lists the key information categories. It is front-loaded, containing no filler or redundant phrasing.

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

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

For a zero-parameter informational tool with an existing output schema, the description is complete. It names the three key content areas (version, data source, known limitations), and the output schema can provide further structural details. There is no additional context needed 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 tool has zero parameters, so the empty schema fully covers the input requirements. The baseline for 0 params is 4, and the description adds no parameter-specific detail, which is appropriate given there is nothing to explain.

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 specifies the tool's purpose: providing version, data source, and known limitations of this server. It is distinct from all sibling tools, which focus on ETF data retrieval, making it immediately recognizable as a server metadata endpoint.

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 explicit guidance on when to use this tool versus alternatives, but the use case is implied: when you need server version, data source, or known limitations. Since no sibling tool serves this purpose, the lack of explicit exclusion is not a major gap.

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