DaedalMap Earthquake Data

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

The annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds minimal behavioral context by noting 'Free discovery' and 'live agent-ready data packs,' which hints at real-time availability and accessibility, but doesn't disclose rate limits, authentication needs, or response format details. 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 highly concise and front-loaded, consisting of a single, efficient sentence that directly states the tool's function without unnecessary words. Every part of the sentence contributes to understanding the tool's purpose.

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 lacks details on output format (e.g., list structure, pagination) and differentiation from siblings. It covers the basic purpose but could be more complete by addressing usage context or behavioral traits beyond 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?

With 0 parameters and 100% schema description coverage, the schema fully documents the input (none required). The description doesn't need to add parameter details, so it appropriately focuses on the tool's purpose. Baseline is 4 for zero parameters, as no compensation is needed.

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 from sibling tools like 'get_pack' or 'query_dataset', which might offer similar data retrieval 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 provides no guidance on when to use this tool versus alternatives. It mentions 'Free discovery' but doesn't clarify if this is for browsing available packs versus querying specific data, nor does it reference sibling tools like 'get_pack' for detailed pack information or 'query_dataset' for data queries.

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 context about being a 'Paid x402 tool' (implying potential cost/access restrictions) and clarifies the two main use cases (aggregate counts vs. raw rows). However, it doesn't disclose rate limits, authentication needs, or detailed behavioral traits beyond what annotations 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 extremely concise with just two sentences that are front-loaded with key information: cost implication and primary use cases. Every word earns its place with zero wasted text.

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 query tool with read-only annotations and no output schema, the description adequately covers the basic purpose and usage. However, it lacks details about response format, pagination, error handling, or how results are structured, which would be helpful given the tool's complexity with 6 parameters including nested objects.

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 100% schema description coverage, the input schema already documents all 6 parameters thoroughly. The description adds minimal value by mentioning 'event_count' and 'event metrics' as examples for the 'metrics' parameter, but doesn't provide additional semantic context beyond what's in the schema descriptions.

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 verb+resource, distinguishing it from siblings like 'get_tsunami_events' or 'get_volcanic_activity'. However, it doesn't fully differentiate from 'query_dataset' which could also query datasets, making it slightly less specific than ideal.

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 explicit guidance on when to use this tool versus alternatives: 'Use event_count for aggregate counts or event metrics for raw event rows.' This helps distinguish between different query types within this tool, though it doesn't explicitly mention when to choose this over sibling tools like 'query_dataset' for broader queries.

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 declare readOnlyHint=true, indicating a safe read operation, which the description doesn't contradict. The description adds context by specifying it's a 'Free tool' and mentions filtering by region and time granularity, but it lacks details on rate limits, authentication needs, or response behavior. With annotations covering safety, this is adequate but not rich in behavioral 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 concise with two sentences that efficiently convey key information: it's free, queries currency data, and uses specific filters. It's front-loaded with the main purpose, though it could be slightly more structured by explicitly listing key parameters upfront.

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 basic purpose and some filtering details but doesn't explain return values, error handling, or how it fits with siblings. With annotations providing safety info, it's adequate but has gaps for a query 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%, so the schema fully documents all 6 parameters. The description adds minimal value by mentioning 'filters.region_ids' and 'filters.time.granularity,' but it doesn't provide additional semantics beyond what's in the schema descriptions. 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 'queries the currency pack' to 'return daily, weekly, or monthly FX data,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_pack' or 'query_dataset,' which might have overlapping functionality, so it misses the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'Free tool' but doesn't explain if this is a distinguishing feature or how it compares to siblings like 'get_catalog' or 'query_dataset.' There's no mention of prerequisites, exclusions, or specific contexts for use.

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

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

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds value by specifying it returns 'detailed metadata, coverage, metrics, and first-query guidance', which gives context on output content beyond just being read-only. However, it doesn't disclose other behavioral traits like rate limits, auth needs, or pagination, keeping the score moderate.

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...'), with no wasted words. Every part contributes to understanding the tool's function and output.

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 mostly complete, covering purpose and output types. However, it lacks details on error handling or exact output structure, which could be useful despite no output schema, slightly reducing 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?

Schema description coverage is 100%, with the parameter pack_id well-documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples or usage tips, so it meets the baseline for high schema coverage without extra value.

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

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

The description clearly states the tool's purpose with specific verbs ('Free discovery', 'Returns') and resources ('detailed metadata, coverage, metrics, and first-query guidance for one pack'), distinguishing it from siblings like get_catalog (likely returns multiple packs) or query_dataset (likely queries data rather than metadata). It explicitly specifies it's for 'one pack'.

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 'Free discovery' and 'first-query guidance', suggesting it's for initial exploration of a pack. However, it doesn't explicitly state when not to use it or name alternatives like get_catalog for broader catalog info or query_dataset for actual data queries, leaving some 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?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds minimal behavioral context: it notes it's a 'Paid x402 tool,' suggesting cost or access restrictions, which is useful beyond annotations. However, it doesn't detail rate limits, authentication needs, or response behavior (e.g., pagination, error handling). With annotations covering safety, this earns a baseline score for adding some value.

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: two sentences that state the tool's purpose and cost implication without unnecessary details. Every sentence earns its place by providing essential information, though it could be slightly more structured (e.g., separating usage hints). It avoids redundancy and waste.

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

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

Given the tool's complexity (6 parameters, nested objects) and lack of output schema, the description is minimally adequate. Annotations cover safety, and schema covers parameters, but the description doesn't address output format, error cases, or integration with siblings. For a query tool with no output schema, more context on return values would be helpful, but it's not critical here.

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 doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain 'metrics' or 'filters' further). Since the schema handles the heavy lifting, a baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'get_earthquake_events' or 'query_dataset', which could help an agent choose correctly in context.

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 it's a 'Paid x402 tool,' which hints at cost implications but doesn't clarify use cases, prerequisites, or comparisons to siblings like 'get_earthquake_events' or 'query_dataset.' This lack of context leaves 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?

The annotation 'readOnlyHint: true' already indicates this is a safe read operation. The description adds minimal behavioral context with 'Free tool' (implying no cost) but doesn't disclose rate limits, authentication needs, or response format details. It doesn't contradict annotations, so a baseline 3 is appropriate given the annotation covers the core safety profile.

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 short sentences) and front-loaded with the core purpose. 'Free tool' adds useful context efficiently. However, the second sentence could be slightly more structured (e.g., clarifying 'volcanoes_events' as a dataset), preventing a perfect 5.

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 minimally adequate. It states the purpose and resource but doesn't cover return values, error handling, or usage examples. With annotations providing read-only safety, it meets a basic threshold but leaves gaps for 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%, so the schema fully documents all 6 parameters. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't elaborate on 'metrics' or 'filters'). With high schema coverage, the baseline is 3 even without extra param info in the description.

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

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

The description clearly states the tool's purpose: 'Queries volcanoes_events for eruption records and volcanic activity metrics.' This 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', 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 mentions 'Free tool' but doesn't explain context, prerequisites, or exclusions. With sibling tools like 'get_earthquake_events' and 'query_dataset' available, the lack of comparative guidance is a significant gap.

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

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds context about pricing (free vs. paid data types via x402) and references the API contract, which is useful behavioral information not covered by annotations. However, it doesn't disclose other traits like rate limits, authentication needs, or response formats, leaving some gaps.

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

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

The description is a single, efficient sentence that front-loads the core purpose and adds pricing context. It avoids redundancy and wastes no words, though it could be slightly more structured for clarity (e.g., separating purpose from constraints).

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) and lack of output schema, the description is somewhat incomplete. It covers purpose and pricing but doesn't explain return values, error handling, or advanced usage scenarios. With annotations only providing readOnlyHint, more behavioral context would be helpful for a query tool with many 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?

Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds minimal semantic context by mentioning source_id and pack_id access and pricing implications for specific pack_ids, but doesn't elaborate on parameter usage beyond what the schema provides. 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 performs 'Generic structured query for direct source_id or pack_id access' and references the API endpoint, providing a specific verb (query) and resource (dataset). It distinguishes from siblings by mentioning direct access via IDs rather than specific endpoints like get_earthquake_events, though it doesn't explicitly contrast with all siblings.

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

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

The description implies usage for querying datasets via source_id or pack_id, with pricing context for different data types (currency/volcanoes free, earthquakes/tsunamis paid). However, it doesn't provide explicit guidance on when to use this tool versus alternatives like get_catalog or specific event tools, nor does it mention exclusions or prerequisites beyond the pricing note.

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