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

With no annotations, the description carries the full burden and does so thoroughly: it discloses the offline/no-data-leaves-host execution, internal metric-to-Japanese mapping, output shape as ranked records with company name and corporate number and ¥ millions, XBRL source, the 192-company limitation, and closest-match rather than error behavior. No annotation is present, but the description gives the agent an accurate behavioral model.

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 dense and front-loaded, with every sentence carrying useful information. It is a long single paragraph, which makes quick scanning slightly harder, but there is no redundancy or fluff; many sentences earn their keep.

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 3-parameter query tool with no output schema and no annotations, the description covers the input formats, the output format (ranked records, specific fields, ¥ millions), the data source, the limitation to 192 blue-chip companies, and the fallback behavior. An agent has enough to invoke and interpret the result 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% and the input schema already explains the metric mapping, fiscal_year omission, and company_name identifiers, including the reliability note. The description repeats those facts but does not add new parameter-level meaning, so it stays at the high-coverage baseline.

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 verb and resource ('Retrieve official EDINET statutory financials ... and executive compensation') and adds accounting-standard normalization context. It also differentiates itself from sibling tools by noting the 192-company index and the closest-match fallback behavior, so an agent can tell it apart from japan_industry_benchmarks and japan_shareholders without inspecting those tools.

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

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

It explicitly says when to use it: 'Use this for a specific financial figure or statement of a named company', and directs to alternatives: japan_company_search for entity discovery and japan_corporate_registry for identity/certification data. It also gives identifier guidance (13-digit corporate number or 4-digit securities code) and sets expectations for out-of-index queries.

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 disclosure burden. It reveals the GUI-mode precondition but does not say what happens when GUI mode is off (error, null, empty string), nor whether any auth or state is required. For a simple read-only getter this is a moderate but real 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?

A single, front-loaded sentence with no filler. The resource ('local Web UI URL for this Orb') is stated before the conditional clause, which is the right ordering.

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-argument getter with no output schema, the description covers the essential facts: what it returns and the condition under which it is meaningful. The remaining gap is the unspecified behavior when GUI mode is disabled, which is minor for a tool this simple.

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 takes zero parameters, so the schema has nothing to document and there is no parameter semantics burden on the description. Baseline of 4 applies for a parameterless tool.

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

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

The description states a specific verb and resource: 'Get the local Web UI URL for this Orb'. It is unambiguous about what is returned. The only sibling, search_knowledge, is unrelated, so no explicit differentiation is needed.

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

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

The phrase 'when GUI mode is enabled' gives an implied precondition for calling the tool, which is useful context. However, it never states what to do if GUI mode is disabled or what alternatives exist, leaving usage guidance partial.

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 disclosure burden and does so well: it states that retrieval runs fully offline, that 'vector' and 'hybrid' require a ~220MB one-time model download, that the Free edition is limited to 192 blue-chip companies, and that results are ranked by relevance. This gives the agent accurate expectations about limitations, side effects, and output shape.

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 dense but every sentence earns its place: purpose, scope, return values, limitations, download caveat, and routing to sibling tools are all present without fluff. It is front-loaded with the core purpose and then layers behavioral and usage details logically.

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 3-parameter search tool with no output schema and no annotations, the description is complete. It explains what the tool returns, when to use it, how to choose methods, what limits apply, what network/download behavior to expect, and which sibling tools to call next. No critical context for invoking the tool correctly is missing.

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

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

Schema coverage is 100% for all three parameters, so the baseline is 3 (schema carries the load). The description adds meaningful method guidance beyond the schema by explaining offline behavior, the download triggered by certain methods, and suitability of 'trigram' vs 'hybrid' for different query types. It doesn't add much for top_k, but the schema already documents it.

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 specific verbs and a resource ('Search across Japanese corporate registries and listed-company filings') and explicitly frames the tool as 'the entry point when the target company is unknown or ambiguous.' It names the return payload (ranked companies with name, corporate number, securities code, snippet), clearly distinguishing it from the detail-oriented sibling tools.

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

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

The description gives explicit when-to-use guidance ('Use this to resolve an entity first') and names the follow-up alternatives exactly: edinet_financials_usgaap, japan_shareholders, and japan_corporate_registry. It also provides method-level selection advice: 'trigram' suits codes and identifiers, while 'hybrid' suits natural-language queries.

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 of behavioral disclosure. It transparently states that the tool runs fully offline, returns a defined set of fields (corporate number, registered name/address, status, gBizINFO records), names its data sources (法人番号公表サイト and gBizINFO), and explicitly warns that out-of-set lookups return nearest matches rather than failing. This level of candor about limitations and output composition exceeds what is typical for a lookup 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?

The description is substantial but each sentence earns its place: it front-loads the core function, then covers coverage/limitations, usage routing, and input reliability. It is not padded, though it could be tightened slightly (e.g., the data sources sentence is somewhat long). Overall it is well-structured and information-dense without being wasteful.

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 lookup tool with no output schema, the description is remarkably complete. It covers what the tool returns, its data provenance, its coverage boundary, the reliability of different input types, and how it relates to sibling tools. An agent has everything needed to decide whether to invoke it and how to construct a correct call; nothing essential 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 both parameters are already documented. The description adds meaningful value beyond the schema by clarifying that a 13-digit corporate number resolves most reliably and by explaining the purpose of info_type ('Which facet to focus on') with concrete facet examples. This reinforces the schema's semantics without redundancy, earning a score above the baseline.

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 a specific verb ('Look up') and a precise resource ('official identity of a Japanese company'), enumerating the exact data points (13-digit corporate number, address, legal status, gBizINFO certifications, subsidies, commendations). It clearly distinguishes itself from sibling tools by explicitly naming edinet_financials_usgaap for financials and japan_company_search for fuzzy discovery, leaving no ambiguity about its 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 gives explicit usage guidance: 'Use this for KYB and entity-verification questions and for 'who or where is this company' lookups' and directly points to alternatives ('use edinet_financials_usgaap for financial figures, or japan_company_search for fuzzy discovery'). It also discloses the coverage limitation (192 blue-chip companies) and advises the most reliable input format (13-digit number or exact Japanese name), which is exactly the kind of operational guidance an agent needs.

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 and discloses important behavior: 'Runs fully offline', 'returns the benchmark row(s) ... with the available metrics and their data year', and 'this Free edition includes a subset of industries'. It also states the data source and the searched-as-is behavior for English names. It doesn't cover failure modes, but the core behavior is well disclosed.

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 dense but not wasteful; each clause contributes metrics, offline behavior, scope limitation, workflow, or input guidance. The main verb and resource are front-loaded, and the later sentences add rather than repeat. It is one long paragraph rather than tight bullets, but it remains efficient.

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 2-parameter read tool with no output schema and no annotations, the description covers the source, the exact metrics, the return content, the edition limitation, the intended companion tool, and parameter conventions. It doesn't describe the precise response shape, but the 'benchmark row(s)' statement gives enough context for invoking the tool.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds value by mapping metric codes to Japanese labels ('operating margin (売上高営業利益率)') and by providing preferred Japanese industry examples beyond those in the schema. This helps an agent supply correct parameter values, especially for metric and industry naming.

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 a specific verb and resource: 'Retrieve sector-level financial benchmarks for Japanese industries'. It names the exact survey source and enumerates the available metrics, making the tool's purpose unmistakable. It also distinguishes itself from company-specific siblings by explicitly framing it for sector benchmarking.

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 an explicit use case: 'benchmark a company's profitability against its sector' and tells the agent to pair it with edinet_financials_usgaap for company-side figures. It lacks explicit 'when not to use' exclusions, but the context is clear enough to route an agent correctly.

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 several behavioral traits: it runs fully offline, covers 192 blue-chip companies, returns nearest matches for out-of-set companies, and explains the return format (ranked list of shareholders with name, shares, ownership percentage, and total shares outstanding). It does not cover error handling or authentication, but for a read-only lookup tool the provided details are adequate. A score of 4 reflects strong disclosure without reaching full completeness.

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 moderately long but every sentence adds value. It leads with the core purpose, then details the return content and coverage limitation, then gives usage guidance and matching advice. The structure is logical and front-loaded, with no redundant or filler sentences. It could be tightened slightly, but it remains efficient and clear.

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

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

For a tool with two parameters, no output schema, and no annotations, the description covers the essential aspects: what it does, what it returns, its limitations (192-company coverage), and how to specify the target company. It also names the relevant sibling tools for adjacent questions. Missing details like error handling or behavior when no match is found are minor for a read-only lookup. Overall, it is sufficiently complete for an agent to call it correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful guidance beyond the schema: it clarifies that company_name can be a Japanese or English name, a 13-digit corporate number, or a 4-digit securities code, and that the numeric forms give the most reliable match. This goes beyond the schema's terse descriptions and helps the agent choose the right input format. top_n is already well described in the schema, so no additional credit needed there.

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 ('Retrieve') and a precise resource ('major shareholders and ownership structure of a Japanese listed company from its EDINET 有価証券報告書 filing'), and explicitly names sibling tools it is not (edinet_financials_usgaap for financial statements, japan_company_search for entity discovery). This clearly distinguishes it from the other tools in the list.

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

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

The description explicitly states when to use this tool ('Use this for ownership, cap-table, and cross-holding questions') and names alternatives with their purposes ('use edinet_financials_usgaap for income-statement or balance-sheet figures, or japan_company_search to discover an entity first'). It also provides matching advice (13-digit corporate number or 4-digit securities code for most reliable match), covering both when and how 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?

No annotations are provided, so the description must carry the full burden. It discloses that 'vector' and 'hybrid' trigger a one-time ~220MB embedding model on first use while 'bm25' and 'trigram' always work offline, and that this Free edition indexes only 192 blue-chip companies. It does not mention caps on top_k or pagination behavior, which amounts to an otherwise minor omission.

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 about 60 words in a single paragraph with a clear topic, method details, domain-alternative guidance, and edge cases. A significant quote here, 'bm25' for exact keywords, is scoped correctly. It is a bit dense and could be split with a sentence dedicated to method guidance, but every sentence contributes valuable information.

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

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

For a tool with no output schema and no annotations, the description covers many things: the result type ('ranked text records'), the content fields (registry, EDINET, major shareholders, gBiz pays, benchmarks), the index limit of 192 companies, and download-required methods. The only gap is an explicit result size/pagination limit, but this is not essential for invoking 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?

All three parameters have descriptions in the input schema, so the baseline is 3. The description adds guidance on method selection – 'bm25' for exact keywords, 'trigram' for codes/identifies, 'vector' for semantic, 'hybrid' for mixed – which gives an agent further reason for choosing a method.

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?

Specific verb and resource: 'Search the Japanese corporate knowledge base' with explicit method mention ('raw keyword, fuzzy, semantic, or hybrid retrieval'). It also frames itself as the 'flexible fallback for open-ended questions the domain tools do not cover' and names the sibling tools as alternatives, making its distinct place in the family clear without opening a schema.

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

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

Provides explicit when-to-use for the domains: 'Prefer the domain tools (edinet_financials_usgaap, japan_corporate_registry, japan_shareholders, japan_industry_benchmarks, japan_company_search) for structured questions; use this for cross-cutting or exploratory queries.'

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