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's a 'Get' operation, implying read-only behavior, but doesn't disclose other traits like rate limits, authentication needs, error handling, or what happens if the ticker is invalid. For a tool with no annotation coverage, this leaves significant 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 is a single, efficient sentence that front-loads the purpose ('Get business description, industry, and background') and specifies scope ('for a US-listed company by ticker'). There is no wasted text, and every word contributes to clarity.

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

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

Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and scope well, but lacks behavioral details (e.g., error cases, return format) and usage guidelines versus siblings. Without annotations or output schema, more context would be helpful 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?

Schema description coverage is 100%, with both parameters well-documented in the schema (exchange with enum values and ticker as case-sensitive). The description adds no additional parameter semantics beyond implying the ticker is for US-listed companies, which is already covered by the schema's exchange enum. Baseline 3 is appropriate when 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 clearly states the action ('Get'), the target resource ('business description, industry, and background'), and the scope ('for a US-listed company by ticker'). It distinguishes itself from siblings like get_stock_data (which likely provides financial metrics) or search_companies (which likely searches by name rather than retrieving by ticker).

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 by specifying 'US-listed company by ticker,' which suggests it's for retrieving profile data rather than market data (vs. get_stock_data) or searching (vs. search_companies). However, it lacks explicit guidance on when to use this tool versus alternatives like get_market_overview or list_tickers, and doesn't mention prerequisites or 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 full burden. It describes what data is returned but doesn't disclose behavioral traits like whether this is a read-only operation (implied by 'Get' but not explicit), whether it requires authentication, rate limits, error conditions, or what happens with invalid dates. For a tool with 4 parameters and no annotation coverage, this is a significant gap in 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, efficient sentence that front-loads the key information: what data is retrieved and for what scope. Every word earns its place with no redundancy or fluff, making it easy for an agent to parse quickly.

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

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

Given no annotations, no output schema, and low schema description coverage (25%), the description is incomplete. It doesn't explain the return format (e.g., structure of sector breakdown), error handling, or prerequisites. For a tool with 4 parameters that returns complex market data, this leaves significant gaps for an agent to use 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 only 25% (only 'stockExchange' has a description), so the description must compensate. It mentions 'exchange on a specific date' which hints at the date parameters but doesn't explain the year/month/day structure, valid ranges beyond schema minimums/maximums, or that only stockExchange is required. The description adds minimal value beyond the schema, failing to adequately document the 3 date parameters.

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 the specific data returned (total market cap, volume, value, performance, sector breakdown) for a specific resource (exchange on a specific date). It distinguishes from siblings like 'get_company_profile' or 'get_sectors_overview' by focusing on exchange-level market metrics rather than company or sector details. However, it doesn't explicitly contrast with 'get_stock_data' which might overlap in some metrics.

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 no guidance on when to use this tool versus alternatives like 'get_sectors_overview' or 'get_stock_data'. It mentions 'sector breakdown' but doesn't explain if this is more detailed than the sibling 'get_sectors_overview'. There are no explicit when-to-use or when-not-to-use statements, leaving the agent to infer context from tool names alone.

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 full burden for behavioral disclosure. It states what the tool does but doesn't describe output format, pagination, rate limits, authentication needs, or error conditions. For a tool with 5 parameters and no output schema, this leaves significant 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 is a single, efficient sentence that front-loads the core functionality without unnecessary words. Every element ('Get aggregated performance metrics by sector for an exchange on a specific date') contributes directly to understanding the tool's purpose.

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 (5 parameters, no annotations, no output schema, low schema coverage), the description is incomplete. It doesn't explain what 'aggregated performance metrics' include, how sectors are defined, date handling for invalid combinations, or the return format. For a data retrieval tool with multiple parameters, more context is needed.

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 only 40%, but the description adds minimal parameter semantics beyond the schema. It implies 'stockExchange', 'year', 'month', 'day', and 'sector' parameters but doesn't explain their relationships or provide additional context like date validation or sector filtering behavior. The description partially compensates but not sufficiently for the low coverage.

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 verb ('Get') and resource ('aggregated performance metrics by sector') with specific context ('for an exchange on a specific date'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_market_overview' or 'list_sectors', which could provide similar sector-related data.

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 no guidance on when to use this tool versus alternatives like 'get_market_overview' or 'list_sectors'. It mentions the context (exchange and date) but doesn't specify prerequisites, exclusions, or comparative use cases with sibling tools.

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 full burden for behavioral disclosure. It mentions what data is returned but doesn't describe error conditions (e.g., invalid ticker or date), rate limits, authentication needs, data freshness, or response format. For a data retrieval tool with 5 parameters and no annotations, this leaves significant 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 is a single, well-structured sentence that efficiently communicates the core function and data scope. It's appropriately sized for this tool type, though it could potentially benefit from a second sentence about limitations or context.

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 5 parameters with only 40% schema coverage and no annotations or output schema, the description provides basic purpose but lacks sufficient context about behavior, error handling, and parameter details. It's minimally adequate for understanding what the tool does but leaves significant gaps for effective use.

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 only 40% (2 of 5 parameters have descriptions). The description adds context by mentioning 'on an exchange and date' which relates to stockExchange, year/month/day parameters, and specifies 'ticker' as the target. However, it doesn't provide additional details about parameter formats, constraints, or interactions beyond what's minimally implied.

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 verb 'Get' and resource 'detailed market data for a specific ticker', specifying what data is included (price, change, volume, etc.). It distinguishes from siblings like get_company_profile (company info) or get_market_overview (broad market data) by focusing on ticker-specific market data. However, it doesn't explicitly differentiate from all siblings (e.g., rank_stocks might also involve ticker data).

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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over get_company_profile (for company info) or search_companies (for finding companies). There's no context about prerequisites, limitations, or typical use cases beyond the basic function.

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 full burden for behavioral disclosure. It states what data is returned but doesn't mention if this is a read-only operation, potential rate limits, authentication needs, data freshness, or pagination behavior. For a tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic output.

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, efficient sentence that front-loads the core action ('Return supported exchanges') followed by specific data fields. Every word earns its place with no redundancy or unnecessary elaboration.

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 has 0 parameters, no annotations, and no output schema, the description provides adequate basic information about what the tool returns. However, it doesn't address behavioral aspects like whether this is a static list or dynamically updated, or how the data is structured. For a simple list tool with minimal complexity, this is minimally viable but could be more complete.

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 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters and focuses on what the tool returns instead.

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 verb ('Return') and resource ('supported exchanges') along with specific data fields returned (IDs, names, country, currency, earliest available date, update frequency). It distinguishes from siblings like list_sectors or list_tickers by specifying it's about exchanges rather than sectors or tickers. However, it doesn't explicitly differentiate from other exchange-related tools (none exist in siblings), so it's not a perfect 5.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or compare to sibling tools like get_market_overview or search_companies. The agent must infer usage from the tool name and description alone without explicit direction.

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 behavioral disclosure. It describes the action ('List available business sectors') and output detail ('including item counts'), but lacks information on permissions, rate limits, pagination, error handling, or data freshness. For a tool with 4 parameters and no annotation coverage, this is a significant gap in behavioral context.

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 efficiently conveys the tool's purpose and key output detail ('including item counts'). It is front-loaded with essential information and has no wasted words, making it easy for an agent to parse quickly.

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

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

Given the tool's complexity (4 parameters, low schema coverage, no annotations, no output schema), the description is incomplete. It adequately states the purpose but lacks usage guidelines, detailed parameter explanations, behavioral traits, and output format information. For a tool that likely returns structured sector data, more context is needed to help the agent use it effectively.

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 low at 25%, with only 'stockExchange' having a description. The description mentions 'for an exchange on a specific date', which hints at the purpose of 'stockExchange', 'year', 'month', and 'day' parameters, but does not explain their semantics, constraints, or interactions beyond what the schema minimally provides. It fails to compensate for the low coverage, leaving most parameters under-documented.

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 verb ('List') and resource ('available business sectors'), specifies the context ('for an exchange on a specific date'), and includes additional detail ('including item counts'). It distinguishes from siblings like 'get_sectors_overview' by focusing on listing with date-specific counts, though the distinction could be more explicit.

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 no guidance on when to use this tool versus alternatives like 'get_sectors_overview' or 'list_exchanges'. It mentions the context ('for an exchange on a specific date') but lacks explicit when-to-use or when-not-to-use instructions, leaving the agent to infer usage based on parameter requirements.

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 behavioral disclosure. It states the tool returns data but doesn't describe output format, pagination, rate limits, authentication needs, or error handling. For a read operation with 6 parameters, this leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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 efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main action and key parameters, making it easy to parse. Every element 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 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address return values, error cases, or practical usage details needed for effective tool invocation. For a data retrieval tool with multiple filters, more context is required to understand limitations and expected behavior.

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 50%, with 3 of 6 parameters having descriptions. The description adds minimal value beyond the schema—it mentions 'exchange', 'specific date', and 'sector' grouping, which aligns with parameters but doesn't explain semantics like date validation, sector filtering logic, or the 'englishNames' default behavior. It partially compensates for the coverage gap but not sufficiently.

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 ('return'), resource ('company tickers and names'), and key constraints ('for an exchange on a specific date, grouped by sector'). It distinguishes from siblings like 'list_exchanges' (which lists exchanges) and 'list_sectors' (which lists sectors), but doesn't explicitly differentiate from 'search_companies' or 'get_company_profile'. The purpose is specific but could better highlight uniqueness.

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 no guidance on when to use this tool versus alternatives like 'search_companies' or 'list_sectors'. It mentions grouping by sector but doesn't specify if this is the primary use case or how it compares to other data retrieval tools. Usage context is implied by the parameters but 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions ranking functionality but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, error handling, or what the output format looks like (e.g., list of stocks with ranks). For a tool with 8 parameters and no output schema, this is a significant 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?

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., separating key constraints). Every element serves a purpose, making it appropriately concise 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?

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral context (e.g., safety, performance), output details, and guidance on parameter usage. While it states the purpose clearly, it doesn't provide enough information for an agent to confidently invoke the tool without additional assumptions.

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 63%, with 5 of 8 parameters having descriptions in the schema. The description adds minimal value beyond the schema: it lists the sortBy metrics (already in schema) and mentions date, order, and limit (implied by parameter names). It doesn't explain parameter interactions (e.g., how sector filtering combines with ranking) or provide usage examples, so it meets the baseline for moderate schema coverage.

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: 'Rank stocks on an exchange by a chosen metric... for a specific date with order and limit.' It specifies the verb ('rank'), resource ('stocks'), and key parameters (exchange, metric, date, order, limit). However, it doesn't explicitly differentiate from sibling tools like 'get_stock_data' or 'search_companies', which might also involve stock data retrieval.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or explain scenarios where ranking is preferred over other data retrieval methods. The agent must infer usage from the purpose alone, which is insufficient for optimal tool 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?

With no annotations provided, the description carries full burden but only states basic functionality. It doesn't disclose behavioral traits like whether this is a read-only operation, rate limits, authentication needs, pagination behavior, or what 'best matches' means algorithmically. The description is minimal and lacks operational context.

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, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or unnecessary elaboration.

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 with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the temporal parameters (year/month/day), the meaning of 'best matches', result format, or error conditions. The description leaves too many operational questions unanswered.

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 50%, and the description mentions 'partial name or ticker' (mapping to query) and 'on an exchange' (mapping to stockExchange). However, it doesn't explain the year/month/day parameters or their purpose in a search context, nor does it clarify the limit parameter's role. The description adds some meaning but doesn't fully compensate for the coverage gap.

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 ('Find companies') and resource ('companies'), specifying search by partial name or ticker on an exchange and returning best matches. It distinguishes from siblings like get_company_profile (detailed view) or list_tickers (full listing), but doesn't explicitly contrast 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 description implies usage for searching companies by name/ticker on exchanges, but provides no explicit guidance on when to use this versus alternatives like list_tickers or get_company_profile. No exclusions or prerequisites are mentioned.

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