DaedalMap Historical FX Rates

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

The description adds some behavioral context beyond the annotations: it specifies that the list includes 'live agent-ready data packs' and implies a discovery function. The annotations already declare readOnlyHint=true, so the agent knows it's a safe read operation. However, the description doesn't disclose details like rate limits, authentication needs, or pagination behavior, leaving gaps in behavioral understanding.

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 with essential information in just two sentences. Every word earns its place: 'Free discovery' sets the context, and 'Returns the list...' clearly states the action and resource. There is no wasted text or 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?

Given the tool's simplicity (0 parameters, read-only, no output schema), the description is adequate but not fully complete. It explains what the tool does but lacks details on output format (e.g., structure of the returned list), error handling, or how it integrates with siblings. For a basic listing tool, this is minimally viable but leaves room for improvement in contextual guidance.

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?

Since there are 0 parameters and schema description coverage is 100%, the baseline is high. The description doesn't need to explain parameters, but it does clarify that this is a 'Free discovery' tool with no inputs required, which aligns with the empty schema. No additional parameter semantics are needed or provided.

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 ('Returns') and resource ('list of live agent-ready data packs available on DaedalMap'), making it easy to understand what it does. However, it doesn't explicitly differentiate itself from sibling tools like 'get_pack' or 'query_dataset', which might have 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 minimal guidance on when to use this tool. It mentions 'Free discovery' which implies a general-purpose listing function, but offers no explicit advice on when to choose this over alternatives like 'get_pack' (which might retrieve specific packs) or 'query_dataset' (which might allow filtering). No exclusions or prerequisites are stated.

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

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds context with 'Paid x402 tool' (implying potential cost/access restrictions) and clarifies metric types, but doesn't detail rate limits, auth needs, or output behavior beyond what annotations cover. No contradiction with annotations.

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, front-loaded with the tool's purpose and usage hint, with no wasted words. However, the 'Paid x402 tool' prefix could be integrated more smoothly, slightly affecting flow.

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 (6 parameters, nested objects) and no output schema, the description is adequate but incomplete. It covers the core query function and metric options but lacks details on response format, error handling, or how filters/sort work, which could aid an agent in proper 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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds minimal value by mentioning 'event_count' and 'event metrics' as examples for the 'metrics' parameter, but doesn't provide additional syntax or format details beyond the schema. Baseline 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 'queries earthquakes_events' with specific metrics options ('event_count' for aggregates or 'event metrics' for raw rows), providing a verb+resource+scope. However, it doesn't explicitly differentiate from sibling tools like 'get_tsunami_events' or 'query_dataset' beyond the domain focus.

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

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

The description implies usage by mentioning 'event_count for aggregate counts or event metrics for raw event rows,' which suggests when to choose metrics but doesn't provide explicit when/when-not guidance or alternatives compared to siblings. No prerequisites or exclusions are stated.

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

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds value by stating 'Free tool,' which implies no authentication or payment requirements, and specifies the data type ('FX data') and granularity options ('daily, weekly, or monthly'). However, it doesn't disclose rate limits, error handling, or data freshness, which are important behavioral traits beyond the annotations. No contradiction with annotations exists.

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 with two sentences: the first states the tool's free nature and core functionality, and the second specifies key filters and output. It's front-loaded with essential information and avoids redundancy. However, the second sentence could be slightly more structured for clarity, and there's minor room for improvement in flow.

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 (6 parameters, nested objects, no output schema) and annotations covering safety, the description is adequate but has gaps. It explains the purpose and key filters but doesn't cover output format, error cases, or how results are structured. With no output schema, more detail on return values would be beneficial, making it minimally complete but not fully 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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds minimal semantics by mentioning 'filters.region_ids' and 'filters.time.granularity,' which aligns with the schema's filter description. It doesn't provide additional details like format examples or default behaviors beyond the schema, so it meets the baseline for high coverage without significant enhancement.

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: 'Queries the currency pack using filters.region_ids plus filters.time.granularity to return daily, weekly, or monthly FX data.' It specifies the verb ('queries'), resource ('currency pack'), and output type ('FX data'), distinguishing it from siblings like get_earthquake_events or get_volcanic_activity. However, it doesn't explicitly differentiate from get_catalog or get_pack, which might also query data packs, so it's not fully sibling-distinctive.

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 mentioning 'filters.region_ids' and 'filters.time.granularity', suggesting it's for filtered FX rate queries. It starts with 'Free tool,' which hints at no cost constraints. However, it lacks explicit guidance on when to use this tool versus alternatives like query_dataset or get_pack, and doesn't specify prerequisites or exclusions, leaving usage somewhat open to interpretation.

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?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds context by specifying it returns metadata and guidance, which is useful beyond the annotation. However, it does not disclose other behavioral traits like rate limits, authentication needs, or error handling. No contradiction with annotations exists.

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 key information ('Free discovery. Returns detailed metadata...'). It avoids unnecessary words and clearly communicates the core functionality 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 low complexity (1 parameter, read-only, no output schema), the description is reasonably complete. It covers the purpose and output types (metadata, coverage, metrics, guidance). However, it could be more specific about the return format or how it differs from siblings to enhance completeness for an AI 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 the parameter 'pack_id' fully documented in the schema. The description does not add any additional meaning or details about the parameter beyond what the schema provides, such as examples or usage tips. Baseline score of 3 is appropriate as the schema handles 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: 'Returns detailed metadata, coverage, metrics, and first-query guidance for one pack.' It specifies the verb ('Returns') and resource ('detailed metadata... for one pack'), but does not explicitly distinguish it from sibling tools like 'get_catalog' or 'query_dataset', which might have overlapping 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 implies usage by mentioning 'Free discovery' and 'first-query guidance', suggesting it's for initial exploration of a pack. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_catalog' (which might list packs) or 'query_dataset' (which might query data within packs). No exclusions or prerequisites are stated.

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

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

The description adds some context beyond annotations: it mentions it's a 'Paid x402 tool,' hinting at potential access or cost considerations. The annotations already declare readOnlyHint=true, indicating a safe read operation, which aligns with 'queries' in the description. However, the description doesn't disclose behavioral traits like rate limits, pagination, or data freshness, leaving gaps 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 concise and front-loaded with key information in two sentences. The first sentence ('Paid x402 tool.') sets context, and the second states the core purpose. There's no unnecessary elaboration, though it could be slightly more structured (e.g., by explicitly mentioning parameters or usage).

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 (6 parameters, nested objects) and lack of output schema, the description is moderately complete. It covers the purpose and hints at access restrictions but doesn't explain return values, error handling, or advanced usage scenarios. With annotations providing safety context (readOnlyHint=true), it's adequate but has clear gaps for a query tool with multiple parameters.

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 doesn't add specific meaning to parameters beyond what the input schema provides. With 100% schema description coverage, the schema already documents all parameters (e.g., 'metrics' for metric ids, 'filters' for structured filters). The description mentions 'tsunami source events and related metrics,' which loosely relates to 'metrics' but doesn't enhance parameter understanding. Baseline 3 is appropriate given 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: 'Queries tsunamis_events for tsunami source events and related metrics.' It specifies the verb ('queries'), resource ('tsunamis_events'), and scope ('tsunami source events and related metrics'). However, it doesn't explicitly differentiate from sibling tools like 'get_earthquake_events' or 'query_dataset' beyond mentioning it's a 'Paid x402 tool'.

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 minimal guidance on when to use this tool. It mentions it's a 'Paid x402 tool,' which might imply usage restrictions, but doesn't specify when to choose this over alternatives like 'get_earthquake_events' or 'query_dataset.' No explicit when/when-not scenarios or prerequisites are provided.

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?

Annotations provide readOnlyHint=true, indicating this is a safe read operation. The description adds minimal behavioral context: 'Free tool' suggests no cost implications, but it doesn't disclose other traits like rate limits, authentication needs, or what 'queries' entails (e.g., pagination, response format). Since annotations cover the safety aspect, the bar is lower, and the description adds some value but not rich behavioral details.

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 and front-loaded with the core purpose in a single sentence. 'Free tool' adds context efficiently. However, it could be slightly more structured by explicitly mentioning the resource type or differentiating from siblings, but it avoids unnecessary 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 complexity (6 parameters, nested objects) and annotations (readOnlyHint=true), the description is minimally adequate. It states the purpose but lacks output details (no output schema provided) and usage context. For a query tool with rich parameters, it should do more to guide the agent, but it meets a basic threshold.

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%, meaning all parameters are documented in the schema. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain 'metrics' or 'filters' further). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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: 'Queries volcanoes_events for eruption records and volcanic activity metrics.' It specifies the verb ('queries'), resource ('volcanoes_events'), and scope ('eruption records and volcanic activity metrics'). However, it doesn't explicitly differentiate from sibling tools like 'get_earthquake_events' or 'query_dataset' beyond the resource name, which is why it doesn't reach 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 mentions 'Free tool' but doesn't explain what this implies or how it compares to siblings like 'get_catalog' or 'query_dataset'. There are no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer usage based on the resource 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?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds valuable context about cost structure (free vs. paid datasets) and references the API contract, which helps the agent understand behavioral constraints. However, it doesn't disclose rate limits, authentication needs, or response format details beyond what annotations already cover.

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 and adds cost context. Every part earns its place, though it could be slightly more structured for clarity. No wasted words or 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?

Given the tool's complexity (8 parameters, nested objects, no output schema) and annotations covering safety, the description is moderately complete. It adds cost context and API reference but lacks details on response format, error handling, or usage examples that would help an agent fully understand the tool's behavior 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 8 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond implying pack_id options ('currency', 'earthquakes', etc.) and source_id examples, which are already covered in schema descriptions. Baseline 3 is appropriate when 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 performs a 'generic structured query for direct source_id or pack_id access' and references a specific API endpoint. It distinguishes itself from siblings by mentioning direct dataset access rather than catalog or specific event types, though it doesn't explicitly contrast with each sibling tool.

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

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

The description implies usage by mentioning 'currency and volcanoes are free; earthquakes and tsunamis are paid via x402,' which provides some context about when to use (based on cost considerations). However, it doesn't explicitly state when to choose this tool over sibling alternatives like get_earthquake_events or get_fx_rates, nor does it provide clear exclusions or prerequisites.

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