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 states the tool 'Get[s]' data, implying a read-only operation, but doesn't mention other traits like authentication needs, rate limits, data freshness, or error handling. This is a significant gap for a tool with no 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?

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, making it easy 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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output format, leaving gaps that could hinder an AI agent's 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?

The schema description coverage is 100%, so the schema already documents all parameters (symbol, period, limit). The description adds minimal value beyond the schema, mentioning 'revenue, EPS forecasts' which hints at output content but doesn't clarify parameter usage or interactions. 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 tool's purpose: 'Get analyst financial estimates for a stock (revenue, EPS forecasts)'. It specifies the verb ('Get'), resource ('analyst financial estimates'), and scope ('for a stock'), but doesn't explicitly differentiate it from sibling tools like 'get_analyst_ratings' or 'get_price_target', which might also relate to analyst 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 sibling tools like 'get_analyst_ratings' or 'get_price_target', which could be related, nor does it specify prerequisites or exclusions. Usage is implied only by the tool's name and description.

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 states the tool retrieves data ('Get'), implying a read-only operation, but doesn't cover aspects like authentication requirements, rate limits, data freshness, or error handling. 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: 'Get analyst ratings and upgrades/downgrades for a stock'. It is front-loaded with the core purpose, contains no redundant information, and every word contributes to understanding the tool's function without waste.

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 (simple read operation with one parameter), 100% schema description coverage, and no output schema, the description is minimally adequate. It states what data is retrieved but lacks details on output format, data scope (e.g., time range, number of analysts), or integration with sibling tools, leaving room for improvement in completeness.

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 100% description coverage, with the 'symbol' parameter documented as 'Stock ticker symbol'. The description adds no additional parameter semantics beyond this, such as format examples (e.g., 'AAPL' for Apple) or constraints. With high schema coverage, a baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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: 'Get analyst ratings and upgrades/downgrades for a stock'. It specifies the verb ('Get'), resource ('analyst ratings and upgrades/downgrades'), and target ('for a stock'). However, it doesn't explicitly differentiate from sibling tools like 'get_analyst_estimates' or 'get_price_target', which might provide related but distinct financial 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 sibling tools like 'get_analyst_estimates' (which might focus on earnings estimates rather than ratings) or 'get_price_target' (which could include price targets from analysts), leaving the agent to infer usage context 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't mention authentication requirements, rate limits, data freshness, or error conditions. For a financial data tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 immediately conveys the core function. There's no wasted verbiage or unnecessary elaboration. It's appropriately sized for a straightforward data retrieval tool.

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 (financial data retrieval with three parameters) and lack of annotations or output schema, the description is minimally adequate. It identifies what data is retrieved but doesn't address behavioral aspects like data format, pagination, or error handling. The agent would need to rely heavily on the input schema and possibly trial-and-error.

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

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

Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds minimal value beyond the schema by implying the tool returns balance sheet data, but doesn't provide additional context about parameter usage or constraints. This meets the baseline for high 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: 'Get company balance sheet statement (annual or quarterly)'. It specifies the verb ('Get') and resource ('company balance sheet statement'), making the function immediately understandable. However, it doesn't differentiate from sibling tools like 'get_cash_flow' or 'get_income_statement' beyond mentioning 'balance sheet' specifically.

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 like 'get_cash_flow' or 'get_income_statement' that serve similar financial reporting purposes, nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does without mentioning any behavioral traits such as rate limits, authentication requirements, data freshness, or what happens on errors. For a tool that likely fetches financial data, this lack of context 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 without any wasted words. It's appropriately sized for a straightforward data retrieval tool, making it easy to scan and understand 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 complexity of financial data tools and the absence of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like data sources, update frequency, or error handling, which are crucial for reliable use. The description alone is insufficient for an agent to fully understand the tool's operation and limitations.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, default behaviors beyond the schema's 'limit' default, or usage nuances. This meets the baseline score of 3 when schema coverage is high.

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: 'Get company cash flow statement (annual or quarterly)'. It specifies the verb 'Get' and the resource 'company cash flow statement', making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_balance_sheet' or 'get_income_statement' beyond the resource name, which is why it doesn't reach a 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 sibling tools like 'get_balance_sheet' or 'get_income_statement' for comparison, nor does it specify prerequisites or contexts where this tool is preferred. The user must infer usage from the tool name and parameters 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information ('Get'), implying a read-only operation, but does not address other behavioral aspects such as rate limits, authentication needs, error handling, or data freshness. This is a significant gap for a tool with no 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?

The description is a single, efficient sentence that front-loads the core action and provides relevant examples without unnecessary details. Every word earns its place, making it highly concise and well-structured.

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 (single parameter, no output schema, no annotations), the description is minimally adequate. It clarifies the tool's purpose but lacks behavioral details and usage guidelines. Without an output schema, it does not explain return values, though the examples hint at data types. It meets basic needs but has clear 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?

The input schema has 100% description coverage, with the single parameter 'symbol' documented as 'Stock ticker symbol'. The description does not add any meaning beyond this, such as format examples or constraints, but the schema adequately covers the parameter, meeting the baseline for high 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 with a specific verb ('Get') and resource ('detailed company profile information'), and lists example fields like description, industry, sector, and CEO. However, it does not explicitly differentiate from siblings like get_quote or get_key_metrics, which might provide overlapping or related data, keeping it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., get_quote for price data, get_key_metrics for financial metrics), there is no indication of context, prerequisites, or exclusions, leaving the agent to infer usage based on 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states 'Get' (implying a read operation) but doesn't mention rate limits, authentication needs, pagination, or what format the calendar data returns. For a tool with 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, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately front-loaded with the core functionality, making it easy 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 simple input schema (two optional parameters with full coverage) and no output schema, the description is minimally adequate but incomplete. It doesn't explain what the earnings calendar data includes (e.g., companies, dates, estimates) or behavioral aspects like rate limits, which are important for a calendar-fetching tool with no annotations.

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 100% description coverage, with both parameters ('from' and 'to') clearly documented as optional date ranges in YYYY-MM-DD format. The description adds no additional parameter semantics beyond what's in the schema, so the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'upcoming earnings announcements calendar', making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_economic_calendar' or 'get_company_profile', but the focus on earnings announcements is specific enough for basic clarity.

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 like 'get_economic_calendar' (for broader economic events) or 'get_company_profile' (for general company info), nor does it specify prerequisites or exclusions for usage.

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 states the tool retrieves a calendar but doesn't describe what the calendar includes (e.g., event types, regions, importance levels), how results are formatted, whether it's paginated, or any rate limits or authentication requirements. This leaves significant gaps for an agent to understand the tool's behavior.

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 with zero wasted words. It front-loads the core purpose ('Get upcoming economic data releases calendar') without unnecessary elaboration, making it easy to parse and understand 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the calendar contains, how results are structured, or any behavioral traits like error handling. For a tool with no structured metadata, the description should provide more context to compensate, but it remains minimal.

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 description adds no parameter semantics beyond what the input schema provides. With 100% schema description coverage, the schema already documents both optional date parameters with formats. The baseline score of 3 reflects that the schema adequately covers parameters, but the description doesn't enhance understanding with examples or contextual usage of date ranges.

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 the resource 'upcoming economic data releases calendar', making the purpose immediately understandable. It distinguishes this tool from siblings like get_earnings_calendar by specifying 'economic data releases' rather than corporate earnings. However, it doesn't explicitly contrast with get_economic_indicator, which might retrieve specific indicators rather than a calendar of releases.

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 siblings like get_earnings_calendar or get_economic_indicator, nor does it specify prerequisites, contexts, or exclusions. The user must infer usage based on the tool name 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?

No annotations are provided, so the description carries full burden. It mentions data retrieval but lacks critical behavioral details: required permissions, rate limits, data freshness, format of returned data, or error handling. 'Get' suggests read-only, but this isn't explicitly confirmed.

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?

Single sentence with zero waste—front-loaded with the core action and resource, followed by clarifying examples. Every word 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?

For a 3-parameter tool with no annotations and no output schema, the description is insufficient. It doesn't explain what data is returned (e.g., time series, single value), handling of optional dates, or error cases. The context signals indicate complexity that isn't 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 description coverage is 100%, so the schema fully documents parameters (name, from, to). The description adds no additional parameter semantics beyond implying the tool handles economic indicators, which is already clear from the tool name and schema examples.

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 ('economic indicator data'), with examples (GDP, unemployment, inflation) that help specify the scope. It distinguishes from most siblings that focus on company/symbol data, though not explicitly from get_economic_calendar.

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 on when to use this tool versus alternatives like get_economic_calendar or get_technical_indicator_*. The description implies it's for historical data retrieval but doesn't specify contexts, 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 the full burden of behavioral disclosure. It states the tool 'gets' data (implying a read-only operation) but doesn't mention any behavioral traits like rate limits, authentication requirements, data freshness, error conditions, or response format. For a financial data tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 extremely concise—a single, efficient sentence that front-loads the core purpose. Every word earns its place: 'Get' (action), 'detailed' (quality), 'financial ratios' (resource), and the parenthetical examples (profitability, liquidity, efficiency) add useful specificity without verbosity.

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 (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context about when to use it, behavioral details, or output information. With no output schema, the description doesn't explain what 'detailed financial ratios' actually returns, leaving the agent to guess the response structure.

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

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

Schema description coverage is 100%, so the schema fully documents all three parameters (symbol, period, limit). The description doesn't add any parameter-specific information beyond what's in the schema—it doesn't explain what 'financial ratios' include or how they relate to the parameters. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need 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 clearly states the tool's purpose: 'Get detailed financial ratios (profitability, liquidity, efficiency)'. It specifies the verb ('Get') and resource ('financial ratios') with categories, making it clear what data is retrieved. However, it doesn't explicitly differentiate from sibling tools like 'get_key_metrics' or 'get_balance_sheet', which might also provide related financial 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. With many sibling tools available (like get_key_metrics, get_balance_sheet, get_income_statement), there's no indication of what makes this tool unique or when it's preferred over others. The user must infer usage from the tool name and parameters 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, the description carries full burden but provides minimal behavioral insight. It mentions 'flexible time intervals' but doesn't disclose rate limits, authentication needs, data freshness, or output format. This leaves significant gaps for a tool that likely involves external data fetching.

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 with no wasted words. It's front-loaded with the core purpose ('Get historical price data') and adds a useful qualifier ('with flexible time intervals'). Every part earns its place.

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

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

For a tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the output contains (e.g., price points, timestamps), error conditions, or behavioral constraints, leaving the agent with insufficient context for reliable 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 100%, so parameters are well-documented in the schema. The description adds little beyond implying time-based filtering, but doesn't explain parameter interactions or default behaviors (e.g., what happens if 'from'/'to' are omitted). Baseline 3 is appropriate as 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 historical price data') and resource ('price data'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_quote' or 'get_technical_indicator_*' which might also provide price-related data, missing explicit distinction.

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

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

No guidance is provided on when to use this tool versus alternatives. The description mentions 'flexible time intervals' but doesn't specify use cases, prerequisites, or exclusions compared to siblings like 'get_quote' for current prices or technical indicators for derived metrics.

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 states the tool retrieves data ('Get'), implying a read-only operation, but doesn't mention any behavioral traits like rate limits, authentication needs, data freshness, or what happens with invalid inputs. For a financial data tool with no annotation coverage, 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 with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without 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 complexity of financial data retrieval, no annotations, and no output schema, the description is incomplete. It doesn't explain return values, error handling, or data format, leaving gaps for an AI agent to understand how to use the tool effectively in context.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (symbol, period, limit) with descriptions and enums. The description adds minimal value by implying period options ('annual or quarterly'), which is already covered in the schema. 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 tool's purpose: 'Get company income statement (annual or quarterly)'. It specifies the verb ('Get') and resource ('company income statement'), and distinguishes the period options. However, it doesn't explicitly differentiate from sibling tools like 'get_balance_sheet' or 'get_cash_flow', which are also financial statement 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_balance_sheet' or 'get_cash_flow' for other financial statements, nor does it specify prerequisites or contexts for choosing this tool over others in the server.

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 states the tool gets 'recent' activity, implying time-bound data, but doesn't specify what 'recent' means (e.g., last 30 days), whether it's read-only, requires authentication, has rate limits, or what the output format is. For a data retrieval tool with zero annotation coverage, this is inadequate.

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 with no wasted words. It's front-loaded with the core purpose, making it easy to parse. Every part of the sentence contributes directly to understanding the tool's function.

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 (financial data retrieval), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like data recency, error handling, or output structure, leaving gaps for an AI agent to use it effectively. The conciseness comes at the cost of necessary detail.

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 description adds minimal meaning beyond the input schema, which has 100% coverage with clear descriptions for 'symbol' and 'limit'. It implies the tool filters by stock symbol and returns a limited set, but doesn't provide additional context like symbol format examples or limit constraints. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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: 'Get recent insider trading activity for a stock.' It specifies the verb ('Get') and resource ('insider trading activity'), and distinguishes it from siblings like get_analyst_ratings or get_balance_sheet by focusing on insider trading. However, it doesn't explicitly differentiate from all siblings (e.g., it's clear but not exhaustive in sibling comparison).

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 (e.g., for investment analysis), or exclusions (e.g., not for real-time data). With many sibling tools for financial data, this lack of usage context is a significant 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions '13F filings' which hints at regulatory data sources, but fails to describe key traits like rate limits, authentication needs, data freshness, or response format (e.g., list of holders with details). This leaves significant gaps for a tool that likely involves external data fetching.

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 with zero wasted words. It front-loads the core purpose ('Get institutional ownership') and includes a clarifying detail ('13F filings') that adds value without verbosity, making it highly concise and well-structured.

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 (financial data fetching with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like data sources, limitations, or return values, which are critical for effective use. The high schema coverage helps with parameters but doesn't compensate for other 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 100%, so the input schema fully documents both parameters ('symbol' and 'limit'). The description adds no additional meaning beyond what's in the schema, such as format examples for 'symbol' or context for 'limit' (e.g., typical ranges). This meets the baseline for high 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 action ('Get') and resource ('institutional ownership (13F filings) for a stock'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_insider_trading' or 'get_balance_sheet', which also provide financial data but for different aspects, so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations (e.g., based on filing dates), or comparisons to siblings like 'get_insider_trading' for different ownership data, leaving usage context implied at best.

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 what metrics are retrieved but lacks critical details: it doesn't specify data sources, rate limits, authentication requirements, error handling, or whether the operation is read-only (implied by 'Get' but not explicit). This leaves significant gaps for an agent to understand operational constraints.

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 with no wasted words. It front-loads the core purpose and includes illustrative examples without unnecessary elaboration, making it easy 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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what metrics are retrieved but lacks details on output format, error conditions, or usage context. Without annotations or output schema, more behavioral and contextual information would improve completeness for safe agent 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 100%, so the schema fully documents all parameters (symbol, period, limit). The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions, default behaviors beyond the schema's 'default: 5' for limit, or how metrics relate to the parameters. Baseline 3 is appropriate when the schema handles parameter documentation.

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: 'Get key financial metrics' with specific examples (P/E, ROE, debt ratios). It uses a specific verb ('Get') and identifies the resource ('key financial metrics'), but does not explicitly differentiate it from sibling tools like 'get_financial_ratios' or 'get_quote', which might offer overlapping functionality.

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. With many sibling tools available (e.g., get_financial_ratios, get_balance_sheet), there is no indication of context, prerequisites, or exclusions to help an agent choose appropriately.

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 'largest price increases' but does not specify timeframes (e.g., daily, weekly), data freshness, rate limits, or authentication needs. This leaves critical behavioral traits undefined for a tool that likely involves real-time or historical market data.

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 directly states the tool's function without unnecessary words. It is front-loaded and wastes no space, 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 complexity of market data tools and lack of annotations or output schema, the description is incomplete. It does not explain return values (e.g., format, fields like stock symbols and percentages), data sources, or error handling, which are essential for proper tool invocation in a financial context.

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 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate here, but it could have optionally mentioned implicit parameters like timeframes or limits, though not required. Baseline is 4 for zero 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 tool's purpose: retrieving stocks with the largest price increases, specifically top gainers. It uses a specific verb ('Get') and resource ('stocks'), but does not explicitly differentiate from its sibling 'get_market_losers' or 'get_most_active', which are related but distinct tools for market 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_losers' or 'get_most_active'. It lacks context about prerequisites, such as whether it requires specific market hours or data sources, leaving the agent to infer usage from the tool name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't specify details like data freshness, rate limits, authentication needs, or what 'largest price drops' means (e.g., time frame, percentage vs. absolute). This leaves significant gaps for an agent to understand the tool's behavior.

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: 'Get stocks with the largest price drops (top losers)'. It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool with no parameters.

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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on output format, data scope, or behavioral traits. With no output schema, it should ideally hint at return values, but the description doesn't, leaving some gaps for an agent to infer usage.

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 parameters with 100% coverage, so no parameter information is needed. The description doesn't add param details, which is appropriate. A baseline of 4 is given as it compensates adequately for the lack of parameters by clearly stating the tool's purpose without redundancy.

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: 'Get stocks with the largest price drops (top losers)'. It specifies the verb ('Get'), resource ('stocks'), and criterion ('largest price drops'). However, it doesn't explicitly differentiate from sibling tools like 'get_market_gainers' or 'get_most_active', which would require a 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 sibling tools like 'get_market_gainers' for gainers or 'get_most_active' for volume-based activity, nor does it specify any context or exclusions for its use.

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

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

No annotations are provided, so the description carries the full burden. It states what the tool does but lacks behavioral details like data freshness, rate limits, authentication needs, or output format. This leaves significant gaps in understanding how the tool behaves in practice.

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 directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'most actively traded' means (e.g., time frame, market scope) or what the return values look like, leaving the agent with insufficient context 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?

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description doesn't add param details, but that's acceptable here. A baseline of 4 is appropriate as the schema fully handles the parameter semantics.

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 ('most actively traded stocks by volume'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from siblings like 'get_market_gainers' or 'get_market_losers', which might also relate to market activity, so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives, such as other market data tools in the sibling list. There's no mention of context, prerequisites, or exclusions, leaving the agent with minimal usage 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't mention any behavioral traits such as rate limits, authentication needs, data freshness, or error handling, which are critical for safe invocation.

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 directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, 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 simple input schema (one parameter) and lack of annotations or output schema, the description is minimally adequate but incomplete. It covers the basic purpose but fails to provide behavioral context or usage guidance, which is needed for a tool in a financial data context with many siblings.

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 100% description coverage, with the 'symbol' parameter clearly documented. The description adds no additional semantic context beyond what the schema provides, such as format examples or constraints, so it meets the baseline score for high schema coverage without extra value.

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

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

The description clearly states the action ('Get') and resource ('analyst price target summary for a stock'), making the tool's purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_analyst_estimates' or 'get_analyst_ratings', which might cover related analyst data, preventing a perfect score.

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

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

No guidance is provided on when to use this tool versus alternatives. With siblings like 'get_analyst_estimates' and 'get_analyst_ratings', the description lacks context on specific use cases, prerequisites, or exclusions, leaving the agent to infer usage based on 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?

No annotations are provided, so the description carries full burden. It mentions 'real-time' which adds context about data freshness, but fails to disclose other behavioral traits such as rate limits, authentication needs, error handling, or what the return format looks like (e.g., price, volume). This is a significant gap for a tool with no 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?

The description is a single, efficient sentence with zero waste, front-loaded with the core purpose and examples. It is appropriately sized for a simple tool with one parameter, making it easy to scan and understand 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 low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate but incomplete. It lacks output details (no output schema provided) and behavioral context like error cases or data sources, which could hinder an agent's ability to use it correctly in varied scenarios.

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 100% description coverage, with the parameter 'symbol' well-documented in the schema. The description adds value by providing examples (AAPL, TSLA, MSFT) that clarify the expected format beyond the schema's generic description, though it doesn't detail constraints like symbol validity or case sensitivity.

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 specific action ('Get real-time stock quote') and resource ('for a symbol'), with examples (AAPL, TSLA, MSFT) that help distinguish it from sibling tools like get_historical_chart or get_company_profile, which serve different financial data purposes.

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 real-time quotes, but does not explicitly state when to use this tool versus alternatives like get_historical_chart for historical data or search_symbol for symbol lookup. No exclusions or prerequisites are mentioned, leaving some ambiguity in 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 provided, so the description carries full burden. It mentions 'current snapshot' which implies real-time or latest data, but doesn't disclose behavioral traits like rate limits, data freshness, authentication needs, or what happens if the date is invalid. For a tool with no annotations, this leaves significant gaps in understanding its 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 a single, efficient sentence with no wasted words. It's front-loaded with the core purpose, making it easy 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 and no output schema, the description is too sparse for a data-fetching tool. It doesn't explain what a 'sector performance snapshot' includes (e.g., metrics, format), how results are structured, or error handling. This inadequately prepares an agent 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 100%, so the schema fully documents the optional date parameter. The description adds no additional parameter semantics beyond implying 'current' data, which aligns with the schema's default behavior. This meets the baseline for high 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 action ('Get') and resource ('current sector performance snapshot'), making the purpose understandable. It doesn't explicitly differentiate from siblings like get_market_gainers or get_sp500_constituents, which also provide market data, but the specific focus on sector performance is reasonably distinct.

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. It doesn't mention prerequisites, timing, or how it differs from other market data tools in the sibling list, leaving the agent to infer usage based on the name 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 the full burden of behavioral disclosure but offers minimal information. It doesn't specify if this is a read-only operation, how frequently the data updates, potential rate limits, authentication needs, or what format the list returns (e.g., symbols, names, weights). This leaves critical behavioral traits undocumented.

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 with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by directly conveying the tool's purpose without redundancy or fluff.

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 financial data tools and the lack of annotations or output schema, the description is incomplete. It doesn't explain what the output contains (e.g., list format, timestamps, metadata), how current the data is, or any limitations. For a tool in a server with many financial siblings, more context is needed to ensure proper 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?

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist, and the description doesn't introduce confusion about inputs.

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 with a specific verb ('Get') and resource ('list of S&P 500 index constituents'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_symbol' or 'get_company_profile' that might also provide stock-related data, preventing a perfect score.

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

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

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't clarify if this is for current constituents only, how it differs from 'search_symbol' for finding S&P 500 stocks, or whether it's suitable for historical data. The description lacks any context about usage scenarios 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 the full burden of behavioral disclosure. It mentions 'latest news articles' but doesn't specify source, freshness, format, pagination, rate limits, or authentication requirements. For a read operation with external data, this lack of context 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 directly states the tool's function without any fluff. It's appropriately sized and front-loaded, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., article titles, dates, sources), potential errors, or behavioral constraints. For a tool fetching dynamic external data, this leaves critical gaps for the 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 clear parameter documentation in the schema. The description adds no additional meaning beyond implying news retrieval for a stock symbol, which the schema already covers with the 'symbol' parameter. This meets the baseline for high 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: 'Get latest news articles for a stock symbol.' It specifies the action (get) and resource (news articles) with the target (stock symbol). However, it doesn't differentiate from sibling tools like 'search_symbol' or 'get_company_profile' that might also provide news-related information, preventing a score of 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. With siblings like 'search_symbol' and 'get_company_profile' that might overlap in functionality, there's no indication of context, prerequisites, or exclusions. This leaves the agent guessing about the best 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does without any additional behavioral traits. It doesn't cover aspects like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the output looks like (e.g., numeric value, chart data). For a tool with no annotations, 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: 'Get Exponential Moving Average (EMA) technical indicator'. It's front-loaded with the core purpose and has zero waste, making it easy to parse quickly. No unnecessary details or redundancy are present.

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 a technical indicator tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral traits, usage context, and output details. While the schema covers parameters well, the description doesn't add enough value to make up for missing annotations and output schema, leaving gaps for an AI agent to understand full tool 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?

The schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, such as explaining how 'period' relates to EMA calculation or typical values. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't compensate with extra insights.

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: 'Get Exponential Moving Average (EMA) technical indicator'. It specifies the verb ('Get') and the resource ('EMA technical indicator'), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'get_technical_indicator_rsi' or 'get_technical_indicator_sma', which would require mentioning it's specifically for EMA rather than other indicators.

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 like 'get_technical_indicator_rsi' or 'get_technical_indicator_sma', nor does it explain the context for choosing EMA over other technical indicators. There's no information on prerequisites, such as needing stock data or specific timeframes, leaving usage unclear.

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 only states what the tool does ('Get RSI') without adding any behavioral context such as data sources, rate limits, authentication needs, error handling, or what the output looks like. This is a significant gap for a tool with no annotation coverage, making it hard for an agent to predict behavior beyond the basic action.

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: 'Get Relative Strength Index (RSI) technical indicator.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a straightforward tool. Every part of the sentence earns its place by clearly stating the action and resource.

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 financial data tools and the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like data freshness, error cases, or output format, which are crucial for an agent to use the tool effectively. The description alone is insufficient for a tool with no structured support beyond the input schema.

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 100% description coverage, with clear documentation for each parameter (symbol, timeframe, period). The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions, default values, or usage examples. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema does the heavy lifting without extra value from the description.

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: 'Get Relative Strength Index (RSI) technical indicator.' It specifies the verb ('Get') and the resource ('Relative Strength Index (RSI) technical indicator'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tools like 'get_technical_indicator_ema' or 'get_technical_indicator_sma' beyond naming the specific indicator.

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 RSI is appropriate compared to other technical indicators (e.g., EMA, SMA) or other financial data tools in the sibling list. There's no context about use cases, prerequisites, or exclusions, leaving the agent with minimal usage 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 full burden but only states what the tool does without behavioral details. It doesn't mention whether this is a read-only operation, if it requires authentication, rate limits, or what the output format looks like (e.g., numeric value, chart data). 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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the essential 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 technical indicator tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., a numeric value, time-series data), how SMA is calculated, or typical interpretation. Given the complexity of financial indicators, more context is needed 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 100%, so parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond the tool name implying SMA calculation. This meets the baseline for high schema coverage but doesn't enhance understanding.

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 ('Simple Moving Average technical indicator'), making the purpose unambiguous. However, it doesn't differentiate from its sibling tool 'get_technical_indicator_ema' (Exponential Moving Average), which would require mentioning SMA specifically distinguishes by calculation method.

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 like 'get_technical_indicator_ema' or 'get_technical_indicator_rsi'. The description lacks context about typical use cases for SMA versus other indicators, leaving the agent without usage 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool is for searching, implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or what the search returns (e.g., list of symbols with details). This is a significant gap for a tool with no 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?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and search criteria, making it 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?

Given the tool's moderate complexity (search function with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., format, fields), potential errors, or behavioral traits like pagination or rate limits, leaving gaps for an agent to 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?

The description adds minimal meaning beyond the input schema, which has 100% coverage and documents the single parameter 'query' as 'Search query (company name or ticker)'. The description restates this but doesn't provide additional context like query syntax, examples, or limitations. With high schema coverage, the baseline is 3.

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 with a specific verb ('search') and resource ('stock symbols'), and specifies what can be searched ('by company name or ticker'). However, it doesn't explicitly differentiate from sibling tools like 'get_quote' or 'get_company_profile', which might also involve stock symbols but serve different purposes.

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, such as needing a query, or compare it to siblings like 'get_quote' (for price data) or 'get_company_profile' (for detailed info), leaving the agent to infer usage context.

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