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 provide readOnlyHint=true, indicating this is a safe read operation. The description adds valuable context about being a 'free live wrapper' and that data is 'preliminary' (not final/canonical), which helps the agent understand data quality and source limitations. However, it doesn't mention rate limits, authentication needs, or what 'normalized to DaedalMap event fields' entails behaviorally.

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 (two sentences) with zero wasted words. It's front-loaded with the core purpose and immediately clarifies what this tool is NOT ('not the enriched canonical history lane'), making efficient use of limited space.

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 (11 parameters, no output schema), the description provides good context about the data source (USGS FDSN API), data nature (live, preliminary), and transformation (normalized to DaedalMap fields). With annotations covering safety and schema covering parameters well, the main gap is lack of output format description, but the description adequately complements the structured data for this read-only 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%, with all 11 parameters well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions, so it meets the baseline of 3 for high schema coverage without providing extra semantic 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 'calls the USGS FDSN API for recent preliminary earthquake events' and 'normalized to DaedalMap event fields', providing specific verb (calls), resource (USGS FDSN API), and transformation (normalization). It distinguishes from 'get_earthquake_events' by specifying 'live' and 'preliminary', but doesn't explicitly contrast with other siblings like 'get_catalog' or 'get_tsunami_events'.

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 with 'live wrapper' and 'recent preliminary earthquake events', suggesting this is for current/real-time data rather than historical analysis. It distinguishes from 'enriched canonical history lane' but doesn't provide explicit when-to-use vs. when-not-to-use guidance or name specific alternatives among the sibling tools.

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.