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. It only states the action and does not disclose return format, potential side effects, rate limits, or other behavioral traits. For example, it does not specify whether it returns a list of operators, coverage maps, or any limitations.

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, front-loaded sentence with no redundant information. It communicates the core purpose efficiently.

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?

The tool is simple with one parameter and no output schema, but the description leaves unclear what 'coverage' entails (e.g., network types, operator lists, or response structure). It is minimally complete for a basic understanding but lacks richer detail expected for an agent to confidently invoke it.

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% coverage with a description for the single parameter ('ISO country code'). The description adds no extra semantics beyond the schema, so the baseline of 3 applies.

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

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

The description uses a specific verb ('check') and resource ('network coverage and operators') for a country. It clearly distinguishes from sibling tools like check_device_compatibility and check_order_status, leaving no ambiguity about its function.

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?

There is no guidance on when to use this tool versus alternatives. The description only states the action without providing context, exclusions, or mention of when it would be preferable to use this tool over siblings like list_countries or check_device_compatibility.

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 exist, so the description must convey behavior. It states the core function and search flexibility, but does not disclose output format (e.g., boolean or list), data source limitations, or whether the check is authoritative. This is a moderate 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 concise (two sentences), starts with the main action, and includes examples without unnecessary words. 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 simple single-parameter read-only tool, the description covers what it does, how to query, and a motivating use case. Missing return value details are a minor gap, but acceptable given the tool's simplicity.

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 already documents the 'query' parameter (100% coverage). The description adds specific examples and a real-world use case, which enriches the parameter semantics beyond what the schema provides.

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 ('Check') and resource ('device supports eSIM'), making it easy to distinguish from sibling tools like check_coverage or search_packages.

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

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

Provides concrete context ('useful to verify before purchase') and gives examples of acceptable queries, but does not explicitly mention alternatives or exclusion scenarios. This gives a clear sense of when to use it, without a thorough comparison to siblings.

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 fully carries the burden. It discloses the coarse return statuses, explicitly states 'no personal data' (privacy behavior), and assures safety by saying 'Safe to poll every few seconds' (non-destructive/idempotent implication). This exceeds typical transparency for a polling tool.

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

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

The description is front-loaded with the core purpose and then provides logically ordered usage details (when to call, return format, polling safety, actions on delivered/failed). Every sentence contributes necessary operational knowledge, with no wasted words.

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 output schema and no annotations, the description compensates fully: it lists the exact possible return statuses, explains polling safety, and instructs what to do on each terminal status. It also notes the absence of personal data. This is a complete specification for an agent to use the tool correctly in a real workflow.

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

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

Schema coverage is 100%, and the schema already describes `session_id` as the Stripe checkout session id returned by `create_checkout`. The description reinforces this connection ('created by `create_checkout`') but does not add new parameter-level detail beyond what the schema provides. Baseline 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 tool checks the status of a Stripe checkout session created by `create_checkout`. It uses a specific verb ('Check'), identifies the resource ('status of a Stripe checkout session'), and references the sibling tool `create_checkout`, effectively distinguishing it from other tools like `check_coverage` or `check_device_compatibility`.

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?

Explicitly instructs when to call the tool: 'You MUST call this after sending payment_url to the user; never trust the user's word that they paid.' It also provides polling guidance ('Safe to poll every few seconds') and conditional actions for `delivered` and `failed` statuses. This gives clear, actionable usage context without ambiguity.

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 and does so thoroughly: it discloses rate limits, mandatory pre-conditions, error behavior ('invalid_activation_mode'), and the requirement to check order status after payment. This goes well beyond what annotations would have provided.

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 long but every sentence delivers critical operational guidance. It is well-structured with clear warnings (MANDATORY, IMPORTANT, POST-STEP) and no filler. The front-loaded purpose and key constraints make it easy 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 complexity (5 params, no output schema), the description covers prerequisites, parameter usage, error handling, rate limits, and required follow-up actions. It mentions the return values needed for post-processing (payment_url, session_id, tax_note, activation_note), making it fully actionable.

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

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

Schema coverage is 100%, but description adds essential meaning beyond schema: customer_country drives currency/VAT, activation_mode explains the practical difference among enums, and the worldwide-plan incompatibility with FIRST_USE. This is exactly the kind of context an agent needs to invoke the tool correctly.

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

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

The description opens with a specific verb+resource: 'Create a Stripe checkout link for an eSIM package.' It clearly distinguishes this from sibling tools like check_order_status or search_packages by focusing on checkout creation.

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

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

Provides explicit mandatory pre-step (know customer's billing country), how to handle it (ask if unknown), optional parameters with context, and a post-step instruction to relay notes and check order status. It also names the alternative tool (check_order_status) and explains the error case for worldwide plans.

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

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

With no annotations, the description carries the transparency burden. It discloses that the output depends on customer_country, affecting display currency and VAT flag, and mentions activation behavior as part of the details. This goes beyond a simple 'get info' and provides useful behavioral context, though it does not explicitly confirm read-only safety.

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 two sentences: the first states the core purpose, the second adds a key usage hint. It is front-loaded, efficient, and contains no extraneous words.

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

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

For a two-parameter, one-required tool with no output schema, the description is reasonably complete. It covers the main outcome (detailed info), key behavioral nuance (customer_country impact), and provides enough context to use the tool correctly. It lacks explicit alternatives or error behavior, but these are not critical for this simple tool.

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

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

Schema description coverage is 100%, and the description repeats the customer_country guidance already present in the schema ('Drives EUR pricing and VAT flag'). It adds no new parameter-specific meaning beyond the schema description, so it meets the baseline for high 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 function: 'Get detailed info about a specific eSIM package, including pricing, VAT/tax note and activation behavior.' It specifies the resource (eSIM package) and the type of information, distinguishing it from sibling tools like search_packages or check_coverage.

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 the usage context: use this when you have a specific package_id and need detailed information. It also provides guidance on the optional customer_country parameter, but does not explicitly mention when not to use this tool or mention alternatives, so it lacks explicit 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?

With no annotations provided, the description carries the full burden. It reveals that the tool lists only countries where eSIM plans are available, which is a precise behavioral trait. For a simple read-only listing with no parameters, this is sufficient transparency; no hidden side effects or permissions need disclosure.

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 with no wasted words. It states the purpose immediately and efficiently.

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 of this simplicity—no parameters, no output schema, and a straightforward listing action—the description is fully complete. It covers all necessary information: what the tool does and what it returns conceptually.

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 zero parameters, so per the rubric the baseline is 4. The description adds no parameter-specific details because none exist, and this is appropriate; there is nothing to clarify beyond the empty schema.

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

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

The description uses a specific verb 'List' and clearly identifies the resource 'countries where eSIM data plans are available.' This unambiguously distinguishes it from sibling tools like check_coverage or get_package_details, which serve different functions.

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 clearly conveys when to use the tool: when you need the list of countries with eSIM availability. While it does not explicitly mention alternatives or exclusions, the context is clear and no alternative tool serves the same purpose, so it provides adequate 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?

Since no annotations are provided, the description carries the full burden. It discloses key behaviors: filtering by data size/validity, customer_country affecting currency/VAT, return limit and default, ordering by price, pagination via offset, and network_count field. It does not list all return fields but gives sufficient 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?

Description is four sentences, front-loaded with the main purpose, and each sentence adds meaningful operational detail. No wasted words.

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?

Covers the core functionality, filters, return behavior, and pagination. Minor gaps: does not specify sort direction for price or behavior on empty results, but given the rich schema and focus of a search tool, it is adequately complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond schema, such as the example 'DE' for customer_country. It mostly reiterates schema info, which is already thorough.

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 specific function: 'Search eSIM data packages by country code.' This distinguishes it from sibling tools like get_package_details (specific package lookup) and list_countries (country listing).

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

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

Provides clear context for when to use the tool (search packages by country with optional filters and pagination). Does not explicitly mention alternatives or exclusions, but the purpose is unambiguous and the context signals (siblings) reinforce differentiation.

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