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 clarify aspects like authentication needs, rate limits, data freshness, or error handling. For a data-fetching tool with zero annotation coverage, this leaves significant gaps in understanding its 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 well-structured and concise, using clear sections ('Args:', 'Returns:') without unnecessary details. Every sentence adds value: the first states the purpose, and the subsequent lines explain parameters and returns efficiently. It's front-loaded with the core functionality, making it easy to scan and understand.

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 the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose, parameter semantics, and return scope. However, the lack of usage guidelines and behavioral details (e.g., data source or update frequency) prevents a perfect score, as these could aid the agent in contextual decisions.

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 meaningful context for the single parameter 'limit' by explaining it as 'Maximum number of recent data points (default: 30).' This clarifies its purpose beyond the schema's technical definition. With 0% schema description coverage, the description fully compensates, providing essential semantic information that aids correct usage.

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 Banxico Reserve Assets data.' This specifies the verb ('Get') and resource ('Banxico Reserve Assets data'), making it immediately understandable. However, it doesn't differentiate this tool from its siblings (like get_cetes_28_data or get_inflation_data) beyond the specific data type, which prevents 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. It doesn't mention sibling tools, context for selecting reserve assets data over other economic indicators, or any prerequisites. The agent must infer usage from the tool name alone, which is insufficient for effective decision-making.

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 that the tool retrieves 'current and historical' data, which implies read-only behavior, but it does not cover important aspects such as rate limits, authentication requirements, data freshness, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operational 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 well-structured and concise, with a clear purpose statement followed by 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it easy to scan and understand quickly. The front-loaded purpose statement ensures the main function is immediately clear.

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

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

Given the tool's low complexity (1 optional parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, parameter semantics, and return scope adequately. However, it lacks behavioral details (e.g., rate limits) and usage guidelines, which slightly reduces completeness for a 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 description adds meaningful context for the single parameter 'limit' by explaining it as 'Maximum number of recent data points (default: 30)', which clarifies its purpose beyond the schema's technical definition. Since schema description coverage is 0%, the description compensates well by providing this semantic information, though it could be more detailed (e.g., explaining what 'recent' means).

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 CETES 28-day interest rate data from Banxico.' It specifies the verb ('Get'), resource ('CETES 28-day interest rate data'), and source ('Banxico'), which is specific and actionable. However, it does not explicitly distinguish this tool from its siblings (e.g., get_inflation_data, get_unemployment_data), 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 does not mention any context, prerequisites, or exclusions, and it does not reference sibling tools like get_date_range_data or get_usd_mxn_historical_data. This lack of usage guidance makes it unclear in what scenarios this tool is preferred over others.

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 mentions the tool retrieves data (implied read-only) and specifies a default series_id, but lacks details on permissions, rate limits, error handling, or response format beyond a vague 'Exchange rate data.' For a data-fetching tool with no annotations, this is insufficient.

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 appropriately sized and front-loaded, with the purpose stated first. The Args and Returns sections are structured but slightly verbose; every sentence adds value, though 'Returns:' could be more concise. Overall, it's efficient with minimal 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 3 parameters with 0% schema coverage and an output schema (implied by 'Has output schema: true'), the description is moderately complete. It explains parameters well but lacks behavioral context (e.g., authentication, errors). The output schema should cover return values, so the vague 'Exchange rate data' is acceptable, but overall gaps remain.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter: start_date and end_date formats (YYYY-MM-DD) and series_id's default value (SF63528 for USD/MXN). This clarifies beyond the bare schema, though it doesn't detail what series_id represents or list alternatives.

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 exchange rate data for a specific date range.' It specifies the verb ('Get'), resource ('exchange rate data'), and scope ('date range'). However, it doesn't explicitly differentiate from siblings like 'get_usd_mxn_historical_data' or 'get_latest_usd_mxn_rate', which appear related.

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 doesn't mention sibling tools, prerequisites, or exclusions. It simply states what the tool does without contextual usage information.

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 the return format ('Formatted inflation data with percentages') but lacks critical details like data source reliability, update frequency, error handling, or rate limits. For a data-fetching tool, this is insufficient 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 appropriately sized and front-loaded, with the core purpose stated first followed by parameter and return details. The structure is clear, though the 'Args' and 'Returns' sections could be integrated more smoothly into prose.

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 parameters, no annotations, but with an output schema), the description is partially complete. It covers purpose and parameters adequately but lacks behavioral context and usage guidelines. The output schema existence reduces the need to detail return values, but overall gaps remain.

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 meaningful semantics for both parameters: it explains that 'inflation_type' specifies the data type (with examples) and 'limit' controls the number of recent data points. Since schema description coverage is 0%, this compensates well, though it doesn't fully document all possible values or constraints (e.g., valid ranges for 'limit').

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 ('inflation data from Banxico'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings (like get_unemployment_data or get_usd_mxn_historical_data) beyond specifying the data type, which prevents 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. It doesn't mention sibling tools or contexts where other tools might be more appropriate, leaving the agent without usage direction beyond the basic purpose.

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

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

No annotations are provided, so the description carries the full burden. It discloses the return format (rate with date) but lacks behavioral details such as rate limits, authentication needs, data freshness, or error handling. This is a significant gap for a tool with zero 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 front-loaded with the core purpose in the first sentence, followed by a concise return statement. Every sentence earns its place with no wasted words, making it highly efficient 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 simplicity (0 parameters, output schema exists), the description is adequate but incomplete. It covers the purpose and return values, but without annotations, it misses key behavioral context like reliability or constraints. The output schema reduces the need to explain returns, but gaps remain.

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 adds value by specifying the data source (Banxico) and return content, which goes beyond the schema. 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 specific action ('Get the latest USD/MXN exchange rate') and the source ('from Banxico'), distinguishing it from sibling tools like 'get_usd_mxn_historical_data' which handles historical data. It precisely identifies both the verb and resource.

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 context by specifying 'latest' exchange rate, suggesting this tool is for current data rather than historical ranges (handled by 'get_usd_mxn_historical_data'). However, it lacks explicit guidance on when not to use it or alternatives for other financial data like reserves or inflation.

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 this is a 'Get' operation but doesn't mention whether it requires authentication, has rate limits, returns errors for invalid series IDs, or has any side effects. The return format is mentioned but without details about structure or potential null values.

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 appropriately sized with clear sections (Args, Returns). The first sentence states the core purpose, followed by parameter and return details. No wasted words, though the structure could be more integrated rather than sectioned.

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 single-parameter tool with output schema, the description covers the basics but has gaps. It explains what the tool does and the parameter, but doesn't address authentication needs, error conditions, or how this metadata tool relates to the sibling data retrieval tools. The presence of an output schema reduces the need to detail return values.

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?

With 0% schema description coverage, the description provides crucial context about the series_id parameter - explaining it's 'The series ID to get metadata for' and providing a specific default example ('SF63528 for USD/MXN'). This adds meaningful semantics beyond what the bare schema provides, though it doesn't explain what constitutes a valid series ID format.

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 metadata') and resource ('for a Banxico series'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this metadata retrieval tool from sibling data retrieval tools like 'get_banxico_reserves_data' or 'get_usd_mxn_historical_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 multiple sibling tools for Banxico data, there's no indication whether this should be used before/after other tools, or how it complements them. The default parameter hint is useful but doesn't constitute usage guidance.

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

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

No annotations are provided, so the description carries full burden. It mentions 'Current and historical UDIS values' but lacks details on behavioral traits such as data freshness, rate limits, authentication needs, error handling, or whether it's a read-only operation. This is a significant gap for a data-fetching 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 appropriately sized and front-loaded, starting with the core purpose, followed by clear 'Args' and 'Returns' sections. Every sentence earns its place by providing essential information without redundancy, making it efficient 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 low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and return scope, though it lacks behavioral context and usage guidelines, which are minor gaps in this 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 description adds meaningful semantics for the 'limit' parameter, explaining it as 'Maximum number of recent data points (default: 30)', which clarifies its purpose beyond the schema's basic type and default. With 0% schema description coverage and 1 parameter, this compensates well, though it doesn't detail data recency or ordering.

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 ('UDIS (Investment Units) data from Banxico'), making the purpose specific and understandable. It distinguishes from some siblings by focusing on UDIS data rather than reserves, exchange rates, or other economic indicators, though it doesn't explicitly differentiate from all similar data-fetching tools like 'get_date_range_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 siblings like 'get_date_range_data' and 'get_historical_data' tools, it's unclear if this tool is for UDIS-specific data, if it has date range limitations, or if other tools might overlap. No explicit when/when-not or alternative usage is mentioned.

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 the tool returns 'Current and historical unemployment rate data,' which implies a read-only operation, but doesn't cover critical aspects like rate limits, authentication needs, data freshness, or error handling. For a data-fetching tool with zero annotation coverage, this is insufficient.

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

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

The description is well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. It's concise with no wasted sentences, though the 'Returns' section is somewhat redundant given the output schema, slightly reducing efficiency.

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) and the presence of an output schema, the description is minimally adequate. It covers the purpose and parameter semantics but lacks behavioral details and usage guidelines. Without annotations, it should do more to explain data scope or limitations, making it just sufficient but not comprehensive.

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 meaningful context for the single parameter 'limit' by explaining its default value (24) and purpose ('2 years of monthly data'), which goes beyond the input schema's basic type and default. With 0% schema description coverage, this compensation is effective, though it could detail format or constraints more.

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 unemployment rate data from Banxico.' It specifies the verb ('Get'), resource ('unemployment rate data'), and source ('Banxico'). However, it doesn't explicitly differentiate from sibling tools like 'get_inflation_data' or 'get_usd_mxn_historical_data' beyond the data type, which keeps 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. It doesn't mention sibling tools or contexts where other tools might be more appropriate, such as using 'get_inflation_data' for inflation metrics or 'get_date_range_data' for broader queries. This leaves the agent without clear usage instructions.

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 fetches historical data but lacks details on rate limits, authentication needs, data freshness, error handling, or pagination. For a data-fetching tool with zero 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 well-structured and concise, with a clear purpose statement followed by brief Arg and Returns sections. Every sentence adds value, and there's no redundant information. It could be slightly more front-loaded by integrating the parameter explanation into the main description, but it's efficient overall.

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 (1 parameter, no annotations, but with an output schema), the description is minimally adequate. The output schema existence means return values needn't be explained, but the description lacks context on data format, time periods, or error cases. It meets basic requirements but leaves 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 description adds minimal semantics for the 'limit' parameter, explaining it as 'Maximum number of recent data points to return (default: 30)'. With 0% schema description coverage, this partially compensates by clarifying the parameter's purpose and default. However, it doesn't detail constraints like valid ranges or what 'recent' means, leaving gaps in 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 historical USD/MXN exchange rate data from Banxico.' It specifies the verb ('Get'), resource ('historical USD/MXN exchange rate data'), and source ('from Banxico'). However, it doesn't explicitly differentiate from sibling tools like 'get_latest_usd_mxn_rate' or 'get_date_range_data', 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_latest_usd_mxn_rate' for current rates or 'get_date_range_data' for date-based queries, nor does it specify use cases or exclusions. This leaves the agent without context for tool selection.

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