DaedalMap Disaster and Geospatial 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 context by specifying it returns 'live agent-ready data packs', which clarifies the data's readiness state. It doesn't disclose rate limits, authentication needs, or pagination behavior, but with annotations covering safety, this provides moderate additional 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 a single, well-structured sentence that front-loads the key action ('Free discovery') and resource. Every word earns its place, with no redundancy or waste, making it highly efficient and 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 simplicity (0 parameters, read-only annotation, no output schema), the description is adequate but minimal. It explains what the tool returns but lacks details on format, pagination, or error handling. For a discovery tool, more context on output structure would enhance 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?

There are zero parameters, and schema description coverage is 100% (empty schema). The description doesn't need to explain parameters, so it appropriately focuses on the tool's function. Baseline for zero parameters is 4, as it efficiently conveys the tool's purpose without unnecessary param 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 ('Returns') and resource ('list of live agent-ready data packs available on DaedalMap'). It distinguishes from siblings by focusing on catalog discovery rather than specific data types like earthquakes or FX rates. However, it doesn't explicitly contrast with 'get_pack' which might retrieve a single 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 for discovery purposes ('Free discovery'), suggesting it's for browsing available data packs. However, it provides no explicit guidance on when to use this versus alternatives like 'query_dataset' or 'get_pack', nor does it mention prerequisites or 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?

The annotation declares readOnlyHint=true, which the description doesn't contradict. The description adds minimal behavioral context beyond this, mentioning it's a 'Paid x402 tool' which hints at potential cost/access restrictions, but doesn't elaborate on rate limits, authentication needs, or response formats.

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, front-loading the cost hint and core functionality. Every word earns its place with no wasted text, making it easy 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?

For a read-only query tool with full schema coverage but no output schema, the description is minimally adequate. It covers the basic purpose and metric usage but lacks details on response structure, error handling, or how it differs from similar sibling tools, 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?

With 100% schema description coverage, the input schema fully documents all 6 parameters. The description adds marginal value by mentioning 'event_count' and 'event metrics' as examples for the 'metrics' parameter, but doesn't provide additional semantics 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 metrics like 'event_count' and 'event metrics', distinguishing it from generic query tools. However, it doesn't explicitly differentiate from sibling tools like 'get_tsunami_events' or 'get_volcanic_activity' beyond the domain name.

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 clear guidance on when to use specific metrics ('event_count for aggregate counts or event metrics for raw event rows'), offering practical usage context. It lacks explicit exclusions or comparisons to alternatives like sibling tools, but the metric guidance is helpful.

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 behavioral context beyond annotations: it mentions 'Free tool' (implying no cost) and specifies routing based on granularity. The annotations already declare readOnlyHint=true, so the agent knows it's a safe read operation. The description doesn't contradict annotations and provides modest additional context, but lacks details on rate limits, authentication needs, 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 concise (two sentences) and front-loaded with key information ('Free tool', purpose). Every sentence contributes: the first sets cost and action, the second details granularity routing. It could be slightly more structured but 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 lack of output schema, the description is moderately complete. It covers the core purpose and granularity routing but doesn't explain return values, error handling, or detailed behavioral traits. With annotations covering read-only safety, it's adequate but has gaps for a data 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 mentions 'filters.time.granularity' and implies it affects data routing, adding slight context beyond the schema's generic filter description. However, it doesn't elaborate on other parameters like 'metrics' or 'output', 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: 'Queries the currency pack' and 'routes by filters.time.granularity to daily, weekly, or monthly FX data.' It specifies the resource (currency pack/FX data) and the action (queries/routes by granularity). However, it doesn't explicitly differentiate from sibling tools like 'get_catalog' or 'query_dataset' beyond mentioning 'currency 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 provides minimal usage guidance. It mentions 'Free tool' and the granularity routing, but doesn't specify when to use this tool versus alternatives like 'get_pack' or 'query_dataset', nor does it outline prerequisites or exclusions. No explicit when/when-not instructions 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 a safe read operation. The description adds value by specifying it's a 'Free live wrapper' and that data is 'normalized to DaedalMap event fields', which gives context about data transformation and cost. 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 concise with two sentences that are front-loaded: the first sentence states the core purpose and data source, while the second clarifies scope. Every sentence adds value without redundancy, making it efficiently structured for quick understanding.

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 with 11 parameters and no output schema, the description is reasonably complete by specifying the API source, data normalization, and temporal scope. However, it could improve by hinting at the return format or data structure, as the output schema is missing. Annotations cover the read-only aspect, but more behavioral context would enhance 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%, so the schema fully documents all 11 parameters. The description doesn't add any parameter-specific semantics beyond what's in the schema, such as explaining interactions between parameters like hours and start_time. Baseline score of 3 is appropriate since the schema handles parameter documentation effectively.

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' with specific verb ('Calls') and resource ('USGS FDSN API'), and distinguishes it from siblings by noting 'This is not the enriched canonical history lane', which differentiates it from tools like get_earthquake_events or get_catalog that might provide historical data.

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 clear context that this is for 'recent preliminary earthquake events' and a 'live wrapper', implying it's for current data rather than historical analysis. However, it doesn't explicitly state when not to use this tool or name specific alternatives among the siblings, though the distinction from 'enriched canonical history' suggests alternatives exist.

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, but the description adds valuable context beyond that: it discloses the data source (Smithsonian/GVP WFS), clarifies it's a 'free live wrapper', mentions normalization to DaedalMap fields, and distinguishes 'preliminary' from 'enriched canonical history'. This gives the agent useful behavioral insights about data freshness and transformation.

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) and front-loaded with the core purpose. Every word earns its place: 'Free live wrapper' sets context, 'Calls the Smithsonian/GVP WFS' specifies source, 'recent preliminary volcanic eruption updates' defines scope, 'normalized to DaedalMap event fields' explains transformation, and the final sentence clarifies what it's not.

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, no output schema), the description provides good context about data source, freshness, and transformation. With annotations covering read-only safety and schema covering all parameters, the main gap is lack of output format details, but the description compensates reasonably with normalization information.

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 8 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating 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 Smithsonian/GVP WFS for recent preliminary volcanic eruption updates' and 'normalized to DaedalMap event fields', which is a specific verb+resource combination. It distinguishes from siblings by specifying 'live' and 'preliminary' updates, differentiating from tools like 'get_volcanic_activity' or 'get_catalog', though not explicitly naming alternatives.

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 stating 'Free live wrapper' and 'recent preliminary volcanic eruption updates', suggesting it's for current/ongoing events rather than historical data. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_volcanic_activity' or provide clear exclusions beyond 'not the enriched canonical history lane'.

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 already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds useful context about what's returned (metadata, coverage, metrics, guidance) and the 'Free discovery' aspect, but doesn't disclose behavioral traits like rate limits, authentication needs, or response format details beyond the listed categories.

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, dense sentence with zero waste—every phrase ('Free discovery', 'detailed metadata', 'coverage', 'metrics', 'first-query guidance', 'for one pack') adds value. It's front-loaded with the core purpose and efficiently communicates scope without 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?

For a simple read-only tool with one well-documented parameter and no output schema, the description provides adequate context about what information is returned. However, it could be more complete by specifying the response structure or linking to sibling tools for data retrieval, given the lack of output schema.

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 pack_id fully documented in the schema. The description doesn't add any parameter-specific semantics beyond implying pack_id identifies a specific pack, which is already clear from 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 specific action ('Returns detailed metadata, coverage, metrics, and first-query guidance') and resource ('for one pack'), distinguishing it from siblings like get_catalog (likely returns multiple packs) or domain-specific tools like get_earthquake_events. The phrase 'Free discovery' further clarifies this is a metadata lookup rather than data 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?

The description implies usage context through 'Free discovery' and 'first-query guidance', suggesting this tool is for exploring pack capabilities before deeper queries. However, it doesn't explicitly state when to use alternatives like query_dataset (for actual data) or get_catalog (for listing packs), nor does it mention prerequisites like needing a pack_id first.

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 behavioral context beyond annotations. Annotations declare 'readOnlyHint: true,' indicating a safe read operation, which the description aligns with by using 'queries.' It also mentions 'Paid x402 tool,' suggesting cost or access implications not covered by annotations. However, it lacks details on rate limits, authentication needs, or response behavior (e.g., pagination), limiting its transparency. No contradiction with annotations is present.

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, consisting of two sentences that efficiently convey the tool's purpose and a key behavioral note ('Paid x402 tool'). There's no unnecessary verbosity, and each sentence adds value. However, it could be slightly more structured by explicitly separating usage guidelines from core functionality, preventing a perfect score.

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 an output schema, the description is moderately complete. It covers the basic purpose and hints at cost, but doesn't explain return values, error handling, or how to interpret results (e.g., what 'tsunami source events' entail). With annotations providing safety info, it's adequate but has clear 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?

The description does not add meaningful parameter semantics beyond the input schema. With 100% schema description coverage, the schema already documents all six parameters thoroughly (e.g., 'metrics' includes examples like 'event_count'). The description only vaguely references 'metrics' and 'filters' without enhancing their meaning. This meets the baseline score of 3, 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: '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'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_earthquake_events' or 'query_dataset', which prevents a score of 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 minimal usage guidance. It mentions 'Paid x402 tool,' which hints at potential cost or access restrictions, but offers no explicit advice on when to use this tool versus alternatives (e.g., 'get_earthquake_events' for different data or 'query_dataset' for broader queries). There's no mention of prerequisites, typical use cases, or exclusions, leaving the agent with little contextual 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?

The description adds minimal behavioral context beyond annotations. Annotations declare readOnlyHint=true, indicating a safe read operation. The description adds that it's a 'Free tool,' which hints at no cost implications, but doesn't disclose rate limits, authentication needs, or what 'queries' entails (e.g., pagination, data freshness). It doesn't contradict annotations, but provides limited additional 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, consisting of two short sentences: 'Free tool. Queries volcanoes_events for eruption records and volcanic activity metrics.' Every word contributes to the purpose, with no wasted information. It could be slightly more structured by explicitly mentioning key parameters, but it's efficient overall.

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 (readOnlyHint=true), but the description doesn't address behavioral aspects like response format, error handling, or usage constraints. It states the purpose clearly but leaves gaps in operational context, making it just sufficient for basic understanding.

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 parameters. The description doesn't add any meaning beyond the schema—it mentions 'eruption records and volcanic activity metrics,' which loosely relates to the 'metrics' parameter but without specifics. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate or enhance parameter understanding.

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 what it retrieves ('eruption records and volcanic activity metrics'). However, it doesn't explicitly differentiate from sibling tools like 'get_earthquake_events' or 'query_dataset' beyond mentioning the specific dataset.

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 siblings like 'get_earthquake_events' and 'query_dataset', there's no indication of how this tool differs in usage scenarios.

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 value beyond the readOnlyHint annotation by mentioning pricing tiers (free vs. paid) and referencing the API contract, which provides context on access constraints. However, it lacks details on rate limits, authentication needs, or response behavior, leaving some behavioral traits undisclosed despite the annotation covering 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 concise and front-loaded, stating the core purpose in the first clause and adding contextual details in a second sentence. There's no wasted text, though it could be slightly more structured for clarity.

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 moderately complete. It covers access and pricing but misses details on response format, error handling, or prerequisites, leaving gaps that could hinder effective use by 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?

With 100% schema description coverage, the input schema already documents all 8 parameters thoroughly. The description adds minimal semantic context by mentioning 'direct source_id or pack_id access' and listing example pack types, but doesn't significantly enhance understanding beyond what the schema provides, meeting 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 performs 'generic structured query for direct source_id or pack_id access' and references the API endpoint, which provides a specific verb (query) and resource (dataset). However, it doesn't explicitly differentiate from siblings like 'get_catalog' or 'get_pack' beyond mentioning specific pack types.

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 'currency and volcanoes are free; earthquakes and tsunamis are paid via x402,' which suggests when certain data might be accessible. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_earthquake_events' or 'get_fx_rates,' leaving usage somewhat implied rather than clearly defined.

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