DaedalMap Wildfire Events

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

Annotations already declare readOnlyHint=true, and description adds 'Free discovery' and 'live agent-ready' context, consistent with read-only behavior. For a simple list retrieval with no parameters, adequate transparency is provided.

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

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

Two sentences, no filler. Front-loaded with 'Free discovery' as a clear indicator. Every word serves a 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 zero parameters and no output schema, the description sufficiently conveys the return of a list of available packs. It could hint at whether results are paginated or limited, but for a simple discovery tool it is complete enough.

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?

Tool has zero parameters; schema description coverage is 100% (no parameters to describe). Baseline for zero params is 4, and description does not need to add parameter detail.

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?

Description uses clear verb 'Returns' and specifies the resource as 'list of live agent-ready data packs on DaedalMap'. It suggests discovery, differentiating from siblings like get_pack (likely for specific pack) and query_dataset (likely for data within packs), though not explicitly.

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?

No guidance on when to use this tool vs siblings. Does not mention that get_pack might be for retrieving details of a single pack, or query_dataset for exploring data. Missing explicit when-to-use/when-not-to-use context.

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 the tool is read-only. The description adds that it expands into a 'bounded' chain, but doesn't elaborate on the bound or other behavioral traits like rate limits or return format. It doesn't contradict the annotation.

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 consists of two concise sentences with no redundant information. The first sentence defines the function, and the second provides usage guidance, achieving efficiency and 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?

The description covers purpose and usage guidelines well. It lacks details about return structure or error conditions, but given the moderate complexity, the presence of annotations and complete schema descriptions makes it sufficiently complete for an AI agent to 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 already documents all parameters. The description reiterates that the event id must be exact and from a supported pack, which aligns with the schema but adds no new semantic meaning beyond what's in the schema.

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

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

The description clearly states the action ('expands'), the resource ('exact disaster event id'), and the result ('bounded related-event chain'). It also specifies the supported packs, distinguishing it from sibling tools like 'search_disaster_links' which may not require an exact id.

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 explicitly states when to use this tool: 'only when you already have an exact event id from a supported pack'. It does not directly mention when not to use it or list alternatives, but the context of supported packs provides implicit guidance.

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

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

Annotations already provide readOnlyHint=true. Description adds 'Free linked-disaster helper' which is ambiguous. No additional behavioral details like auth, rate limits, or side effects.

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?

Two sentences, front-loaded with key purpose. No wasted words. Efficient and clear.

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

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

Given no output schema, the description conveys it returns related-disaster links. It could clarify what fields the links contain, but it's adequate for a simple resolver.

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 covers 100% of parameters with descriptions. Description adds some context (exact id, supported packs) but does not significantly enhance understanding beyond schema.

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

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

The description clearly states it resolves an exact disaster event id into related-disaster links. It uses specific verbs and resources, but does not explicitly differentiate from siblings like get_disaster_link_chain, though it hints at the use case.

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

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

Explicitly says to use this only when you have an exact event id from a supported pack. Does not list when not to use or alternatives, but the sibling context provides that.

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 tool is known to be non-destructive. The description adds value by detailing the returned information (metadata, coverage, freshness, guidance, examples), which goes beyond what annotations provide.

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 concise sentence that is front-loaded with 'Free discovery.' and immediately lists what is returned. Every word adds value, with no 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 (one parameter, no output schema, clear annotations), the description fully covers the necessary context: what it returns, when to use it, and how it relates to the querying workflow. No additional details are needed.

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 a single parameter (pack_id) fully documented in the schema. The description does not add new information about the parameter beyond its role in the tool's purpose, meeting the baseline.

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 states 'Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack.' It clearly specifies the action (returns) and the resource (pack), distinguishing it from siblings like get_catalog (which lists packs) and query_dataset (which queries 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?

Description explicitly says 'Call this before querying a new pack' which sets clear context for when to use. It does not explicitly state when not to use or provide alternatives, but the purpose and sibling names imply the tool is for discovery before querying.

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 tool's non-destructive nature is clear. The description ('Generic structured query') adds no further behavioral context beyond what annotations provide. With annotations covering the safety profile, a score of 3 is appropriate.

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: one defining the tool's purpose and one listing packs. It is front-loaded and efficient, though it could be slightly more structured.

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 adequately covers the essential usage and pack catalog. It omits return format details, but the rules state that without an output schema, the description needn't explain return values. It is complete enough 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?

The schema already has 100% description coverage, but the description adds valuable context by listing free and paid packs, which helps agents select appropriate pack_id or source_id values. This enrichment justifies a 4 rather than a 3.

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 identifies the tool as a means to query datasets by source_id or pack_id, specifying the contract URI and listing available packs. This distinguishes it from sibling tools like get_catalog or get_pack, which likely serve different purposes.

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 differentiates free vs paid packs, hinting at usage context, but does not explicitly state when to use query_dataset over alternatives like get_catalog or get_pack. The guideline is implied but not explicit, leaving some ambiguity for an AI agent.

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, and the description calls it a 'discovery helper', implying no side effects. The description adds no additional behavioral context beyond the annotation, so a score of 3 is appropriate.

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?

Two clear sentences with no wasted words. The first sentence captures the core action and parameters, making it easy to scan.

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

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

The description covers the search functionality but does not explain the return format or pagination behavior. Given no output schema, more detail on what 'matching chains' means would elevate 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 coverage is 100% with each parameter having a description. The main description only restates high-level categories (event type direction, year window) without adding new semantics, so baseline 3 applies.

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

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

The description clearly states the tool searches for cross-disaster link families by event type, direction, and optional year window. It distinguishes from sibling tools by specifying discovery before having an exact event ID.

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

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

Explicitly advises using this when exploring relationships without an exact event ID, implying alternatives exist. However, it does not name specific sibling tools or state explicit when-not-to-use conditions.

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