AskMia.app travel eSIM AI agent

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 what the tool does but doesn't add context like whether it's read-only, requires authentication, has rate limits, or what the output format might be. This leaves significant gaps in understanding the tool's behavior beyond its basic function.

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 waste. It's appropriately sized and front-loaded, making it easy to 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 has no annotations, no output schema, and the description lacks behavioral context, it's incomplete for effective use. The agent knows what it does but not how to interpret results or handle errors, which is insufficient for a tool that might involve network queries or data retrieval.

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 'country_code' parameter documented as an ISO country code. The description implies this parameter is used for checking coverage but doesn't add meaning beyond what the schema provides, such as examples or constraints, so it 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 as checking network coverage and operators for a country, which is specific (verb+resource). However, it doesn't distinguish from sibling tools like 'list_countries' or 'search_packages', which might be related but serve different functions, so it lacks explicit sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as whether it's for pre-purchase validation or general lookup, leaving the agent to infer usage without explicit direction.

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

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

With no annotations provided, the description carries the full burden and adds valuable behavioral context: it discloses the post-payment delivery method (email), account creation automation, and rate limits (2/min open access, 60/min with API key), though it lacks details on error handling or response format.

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 and efficiently covers key behaviors in three sentences with zero waste, 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 complexity (payment processing with automation) and lack of annotations/output schema, the description is mostly complete but could benefit from mentioning response structure or error cases to fully guide an agent.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description implies the purpose of package_id and customer_email but does not add significant meaning beyond what the schema provides, such as format examples or constraints.

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 ('Create a payment link for an eSIM package') and resource ('eSIM package'), distinguishing it from sibling tools like check_coverage or get_package_details by focusing on payment processing and delivery rather than information retrieval.

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

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

It provides clear context for when to use this tool (to generate a payment link for eSIM delivery) but does not explicitly mention when not to use it or name specific alternatives among sibling tools, such as for non-payment-related tasks.

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 read operation ('Get'), implying safety, but doesn't address authentication needs, rate limits, error conditions, or what 'detailed info' includes (e.g., fields returned). This leaves significant gaps for a tool that presumably queries sensitive package 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 communicates the core purpose without redundancy. It's appropriately front-loaded and wastes no words, making it easy for an agent to parse quickly.

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

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

Given the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what 'detailed info' entails (e.g., pricing, data limits, validity), potential side effects, or error handling. For a tool that likely returns structured package data, this omission hinders effective 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%, with the single parameter 'package_id' documented in the schema. The description adds no additional parameter context (e.g., format examples, where to find package IDs, or validation rules), so it meets the baseline for adequate but unhelpful 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 action ('Get detailed info') and target resource ('specific eSIM package'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search_packages' or 'check_coverage' that might also retrieve package information, 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?

The description provides no guidance on when to use this tool versus alternatives like 'search_packages' or 'list_countries'. It lacks context about prerequisites (e.g., needing a package ID) or typical use cases, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists countries but does not mention any behavioral traits such as whether it requires authentication, has rate limits, returns paginated results, or includes error handling. This leaves significant gaps in understanding how the tool operates beyond its basic function.

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, clear sentence that front-loads the core action ('List all countries') and specifies the scope ('where eSIM data plans are available'). There is zero waste or redundancy, making it highly efficient and easy to parse for an AI agent.

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 complete enough to convey the basic purpose. However, it lacks details on behavioral aspects like response format or error conditions, which could be important for an agent to use it effectively. The absence of an output schema means the description should ideally hint at return values, but it does not, leaving some contextual 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 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description implies no parameters are needed by stating 'List all countries', which aligns with the schema. Since there are no parameters, the baseline score is 4, as the description adequately conveys the parameterless nature without adding unnecessary details.

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 ('List') and resource ('countries where eSIM data plans are available'), making it immediately understandable. However, it does not explicitly differentiate from sibling tools like 'search_packages' or 'check_coverage', which might also involve country-related queries, leaving some ambiguity about its unique role.

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 'search_packages' or 'check_coverage', which could also relate to country availability. It lacks explicit context, prerequisites, or exclusions, leaving the agent to 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 the full burden of behavioral disclosure. It mentions filtering options but lacks critical details such as whether this is a read-only operation, how results are returned (e.g., pagination, sorting), error handling, or rate limits. For a search tool with no annotation coverage, this is a significant gap in transparency.

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

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

The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and optional filtering. Every word contributes essential information without redundancy or fluff, making it highly efficient for an agent to parse.

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 with two parameters) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose and parameters but omits behavioral details like result format, error cases, or usage context relative to siblings. This leaves gaps that could hinder effective tool invocation.

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 both parameters ('country_code' and 'min_data_gb') with descriptions. The description adds minimal value by restating the optional filtering for 'min_data_gb' but doesn't provide additional context like format examples or constraints beyond what's in the schema. 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: 'Search eSIM data packages by country code. Optionally filter by minimum data size in GB.' It specifies the verb ('search'), resource ('eSIM data packages'), and key filtering parameters. However, it doesn't explicitly differentiate from sibling tools like 'list_countries' or 'get_package_details', 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 like 'list_countries' (which might list available countries) or 'get_package_details' (which might retrieve details for a specific package), nor does it specify prerequisites or exclusions. This leaves the agent with minimal 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.