VLSI Korea Semiconductor Data

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 the safety profile is covered. The description adds useful disclosure about what is returned: grouped issues with title, URL, excerpt and date, across all language editions. It does not discuss freshness or caching, but that is a minor gap given the annotation coverage.

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 compact sentences, front-loaded with the resource and contents, then the usage cue. Every sentence earns its place with no 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 parameters, no output schema and a clear read-only annotation profile, the description supplies everything needed: what the tool returns, how items are grouped, and when to reach for it first. It is complete for an agent to call the tool correctly.

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

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

The tool has zero parameters, so there is no parameter semantics to explain. The baseline for a parameterless tool is 4, and nothing in the description undermines that.

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 and resource ('Get ... daily semiconductor briefing') and enumerates the exact content included: latest Daily Silicon issue in all language editions plus latest Market Bulletin, with title, URL, excerpt and date. This clearly distinguishes it from sibling tools like get_latest_articles and get_news_feed.

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?

'Start here for today's semiconductor knowledge briefing' gives clear context for when to use the tool as an entry point. It does not explicitly name alternatives or exclusions, but the stated scope is specific enough that overlap with siblings is minimal.

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 closed-world behavior, so the description's job is lighter. It adds genuinely useful context the annotations don't: the index is weekly, job-posting based, Korea-scoped, and 'latest' is the default. It does not say what happens with an unknown or out-of-range week key, or how stale the 'latest' value can be.

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 identity and scope front-loaded and no filler. The second sentence largely duplicates the schema's parameter description, which is the only wasted 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?

For a one-optional-parameter read tool with full annotations, the definition is nearly sufficient. However, there is no output schema, so the description is the only place an agent could learn what the index value actually looks like (units, baseline, components), and it stays silent on that.

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% and the single parameter's meaning is fully documented in the schema. The description restates the same 'latest'/'2026-W42' format without adding anything new, such as how week boundaries are defined or whether a malformed key errors or falls back.

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?

Names the specific resource (VLSI Semiconductor Hiring Index), its cadence (weekly), domain (semiconductor hiring activity, job-posting based) and geography (Korea). That is enough to distinguish it from siblings like get_market_movers or get_news_feed, though it never explicitly contrasts itself with any of 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?

The only usage instruction is about the parameter ('use week=latest or a key like 2026-W42'), which is schema content, not selection guidance. There is no statement of when this tool is the right choice, when it is not, or what to use instead.

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, non-destructive, and closed-world, so the safety profile is fully covered. The description adds only the 'newest first' ordering guarantee, which is modest added value; it says nothing about pagination, total counts, or how many articles may exist beyond the limit.

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?

Short overall and front-loaded with the core action, but the idea of 'latest' is restated three times ('newest published', 'newest first', 'published most recently'), so the closing sentence earns little. Two of three sentences survive trimming.

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?

A simple two-optional-param read tool with full annotation coverage and complete inline schema docs needs little more; this description is adequate. The only real gap is the missing disambiguation from get_daily_briefing and get_news_feed given how many sibling feed-like tools exist.

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% — both params are documented inline, including the language list and the 1-50 limit range. The description's language enumeration duplicates the schema verbatim and adds no new semantics, so the baseline 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?

States a specific verb and resource ('Get the newest published VLSI Korea semiconductor articles') plus the sort order, which is more than the name alone conveys. It implicitly distinguishes itself from search_articles by having no query, but never names that sibling or get_news_feed, so differentiation is left to inference.

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?

'Call it to see what VLSI Korea published most recently' gives a usage context, but there are no exclusions and no routing guidance versus search_articles, get_news_feed, or the confusingly similar get_daily_briefing. The phrase 'daily semiconductor knowledge briefing feed' actually blurs the boundary with the get_daily_briefing sibling rather than sharpening 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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful behavioral details beyond the schema: the calculation source (last two daily closes), descending sort order, delayed EOD data, and a non-advice disclaimer.

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 compact sentences. The core listing behavior, sorting, direction, and defaults are front-loaded, followed by the data-latency disclaimer.

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 list tool with full schema coverage and no output schema, the description is nearly complete. It does not describe the returned record shape or result limit, but the essential calling behavior is covered.

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. However, the description clarifies the calculation behind threshold_pct ('computed from the last two daily closes') and restates direction semantics with the default, adding some meaning beyond the schema 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 names a specific action and resource ('List semiconductor stocks') plus the exact selection criterion (absolute daily change at or above a threshold). It is clearly distinguishable from singular-quote or prediction siblings such as get_stock_quote and get_predictions.

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 clear usage context: the default direction, the default threshold, and that the output is sorted by absolute change. It does not explicitly name alternatives or when-not-to-use cases, but the scope is unambiguous.

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, and closed-world, so the safety profile is covered. The description adds real behavioral information beyond that: the snapshot model and the 'not yet generated' status when no feed exists for a date. It does not describe freshness or what the feed contains, but for a simple read that is a minor gap.

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, zero filler, with the domain and the date keying front-loaded and the edge-case status disclosed second. Nothing could be cut without losing 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 one-parameter read-only tool with a fully described schema and full annotation coverage, the description plus structured fields are nearly sufficient. The remaining shortfall is sibling disambiguation — the agent still cannot confidently choose this over get_daily_briefing or get_latest_articles from the text alone.

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 a single parameter with 100% schema description coverage, so the schema already documents 'latest' vs 'YYYY-MM-DD'. The description repeats that accepted format without adding semantics such as timezone, how far back dates are valid, or what 'latest' resolves to.

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 (a VLSI Korea semiconductor news intelligence feed) and the keying dimension (a date). An agent can tell what it retrieves, but nothing distinguishes it from close siblings like get_daily_briefing, get_latest_articles, or get_predictions, which plausibly return overlapping content.

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 only implied by the accepted date values and the note about a 'not yet generated' snapshot. There is no explicit statement of when to prefer this over get_daily_briefing or get_latest_articles, and no exclusions or prerequisites.

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 fully covered without the description. The description adds that the data is a historical 'track record' with outcomes, which helps set expectations for results, but says nothing about result volume, ordering, or recency.

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: the resource and its contents come first, the filtering mechanics second. No filler, no 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 single-optional-enum read tool with rich annotations, the description covers what the data is and how to filter it, which is enough to call it correctly. Without an output schema, a brief note on what a prediction record contains (e.g., call, date, outcome) would have closed 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?

Schema coverage is 0%, but the sole parameter is an enum with a stated default, and the description enumerates the same values plus clarifies that 'all' is the default. It adds little beyond the enum itself — it does not explain what 'pending' vs 'hit'/'miss' means in terms of outcome evaluation, so the compensation for zero coverage is only partial.

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 resource — VLSI Korea's public prediction track record — and clarifies what a prediction is ('semiconductor market calls with outcomes'), which an agent could not infer from the name alone. It is clearly distinct from siblings like get_news_feed or get_stock_quote, though it never explicitly contrasts itself with 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?

The 'Filter by status' sentence implies how to narrow results and gives the default, which is useful context for invocation. There is no explicit statement of when to choose this tool over siblings (e.g., articles vs. predictions) or any prerequisite, so usage is only implied.

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/closed-world, so the safety profile is covered. The description adds genuinely new context: data is delayed end-of-day, change % is derived from the last two daily closes, and it is not investment advice. It stops short of saying what happens for out-of-coverage tickers or what fields are returned.

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?

Four tight sentences, front-loaded with purpose and scope, with no filler. The ticker-format example and change-% computation detail both earn their place.

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

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

With no output schema, the description does mention the computed daily change %, but it doesn't sketch the returned fields (price, currency, date) an agent would need to consume the result. Coverage, freshness, and input semantics are all otherwise fully addressed.

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% and the single 'query' parameter is documented in-schema. The description restates the three matching modes with examples ('000660.KS', 'SK hynix'), which is useful reinforcement but largely overlaps the schema, so baseline 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?

States a specific verb and resource ('Get an end-of-day quote for a semiconductor stock') plus an explicit scope ('VLSI Korea market data, ~113 tickers'). This clearly separates it from the news/article/briefing siblings without requiring schema inspection.

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 constraints ('semiconductor stock covered by VLSI Korea', ~113 tickers) implicitly tell the agent when this tool applies and when it won't. It gives no explicit alternative routing or when-not guidance, but the coverage boundary is clear enough for correct selection.

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 this a read-only, idempotent, non-open-world operation; the description adds genuine context beyond them: the glossary's size, partial-match semantics, and that the output is a translation in the requested language. It does not address multiple partial matches or case sensitivity, which keeps it below a 5.

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 zero filler; the core behavior and the key constraint (English term, partial match) are front-loaded before the return-value detail.

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

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

With no output schema, the description carries the return-value burden and does so adequately by stating that a translation in the requested language is returned. Minor residual gaps: behavior on multiple matches and what happens when the requested language is not available.

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 value: it enumerates the valid language codes (en, ko, ja, zh_tw, es, pt, id, vi) for a `lang` parameter whose schema has no enum, and clarifies `term` must be English despite partial matching.

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 and resource (look up semiconductor terms in the VLSI multilingual glossary), quantifies scope (400+ terms), and pins down matching behavior (partial match on English term) and return (translation in requested language). Clearly distinguishable from the news/market/qna 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 describes the input expectation (English term, partial match) which implicitly guides usage, but gives no explicit when-to-use/when-not framing or routing to an alternative. Since no sibling is a plausible substitute, the omission is minor but still a gap.

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 read-only, idempotent, non-destructive, closed-world behavior. The description adds useful context beyond that: corpus size, the fields searched, title-match ranking priority, and the fields returned.

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 compact sentences with the key purpose, ranking rule, filter option, and return shape front-loaded. Every sentence carries useful information with 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 search tool without an output schema, the description covers corpus size, search scope, ranking behavior, filtering, and returned fields. Nothing essential for correct invocation 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 description coverage is 100%, so the query, lang, and limit parameters are already fully documented. The description repeats the language list but adds no syntax, format, or constraint details 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?

States a specific verb and resource ('Search ... articles') plus the searchable fields and ranking behavior. It is clearly distinguishable from sibling retrieval tools such as get_latest_articles and get_news_feed.

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 keyword-search framing implies when to use it over browsing latest articles or the news feed, and it explains the optional language filter. It does not explicitly name alternatives or when not to use it, so it stops short of full routing 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 establish readOnlyHint, idempotentHint, destructiveHint=false, and closed-world scope, so the safety profile is fully covered. The description adds genuine behavioral detail beyond that: matching is keyword-based over title+question+answer specifically, and the result set is capped at top 5 with excerpts, telling the agent exactly what shape of answer to expect.

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, no filler, and the front half establishes identity before the mechanical detail. Every clause earns its place: scope, matching behavior, result count, and result form.

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 search tool with full annotation and schema coverage, the description covers what an agent needs to call it and anticipate the response. Minor gaps remain: no mention of result ordering/relevance or what happens on an empty result set, though a fixed top-5 return makes pagination notes unnecessary.

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 there is a single required parameter, so the schema already carries the syntax burden. The description still adds real meaning by disclosing which fields the query string is matched against (title, question, answer), which the schema does not say.

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 (search) and resource (VLSI Korea's semiconductor Q&A knowledge base), reinforced with concrete domain examples (STA, HBM, packaging, EDA). This is clearly distinguishable from content-oriented siblings like search_articles and the term-definition tool lookup_term.

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 only implied: an agent can infer this is the keyword-search entry point into Q&A content, but there is no explicit when-to-use or when-to-prefer-a-sibling statement (e.g. lookup_term for a single concept vs search_articles for published articles). The reader must infer the routing from the tool name and the resource description.

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