DaedalMap Hurricane and Tropical Cyclone Data

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

The description adds valuable context beyond the readOnlyHint annotation by specifying that it returns 'live agent-ready data packs' and that this is for 'discovery' purposes. While annotations cover the safety aspect (read-only), the description provides operational context about what kind of data is returned and the tool's discovery role.

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 perfectly concise with two clear, front-loaded sentences. 'Free discovery' immediately establishes context, and 'Returns the list...' completes the functional explanation. Every word earns its place with zero 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 zero-parameter read-only tool without an output schema, the description provides adequate context about what the tool does and what it returns. However, it could be more complete by specifying the return format (e.g., list structure, metadata included) or any limitations of the 'live agent-ready' qualification.

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 baseline would be 4. The description appropriately doesn't waste space discussing non-existent parameters, and the phrase 'Free discovery' provides useful semantic context about the tool's zero-parameter nature as an exploration function.

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 ('list of live agent-ready data packs available on DaedalMap'). It distinguishes itself from siblings like get_earthquake_events or get_fx_rates by focusing on catalog discovery rather than specific 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 for discovering available data packs, but provides no explicit guidance on when to use this tool versus alternatives like get_pack or query_dataset. The phrase 'Free discovery' suggests a preliminary exploration function, but lacks clear when/when-not instructions or named alternatives.

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 set readOnlyHint=true, and the description reinforces this by calling it a 'free discovery' and describing its return as metadata only. No contradiction. The description adds value beyond annotations by detailing what the output includes (coverage, freshness, guidance, examples), which is critical since there is no output schema.

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: the first efficiently states what the tool returns, and the second provides usage context. Every sentence earns its place, with no fluff. It is front-loaded 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 (one parameter, no output schema, read-only), the description covers the essential aspects: what it returns and when to use it. Annotations provide the safety profile. It is nearly complete, though a mention of the tool being free (no cost) is implied but not explicit. Still, it is sufficient for 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?

There is only one parameter, pack_id, and the input schema already provides a list of example values with 100% coverage. The description adds little beyond stating that the tool operates 'for one pack,' which is consistent with the parameter. Per guidelines, with high schema coverage, 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 it returns detailed metadata, coverage, freshness, canonical tool guidance, and first-query examples for one pack. The verb 'returns' and specific resource 'metadata for one pack' make the purpose unambiguous. It distinguishes itself from siblings like get_catalog (listing all packs) and query_dataset (running queries) by positioning itself as a preliminary discovery step.

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 tells the agent to 'Call this before querying a new pack' and explains the benefits: seeing time shape, coverage limits, and a paste-ready first query. While it lacks explicit when-not-to-use or alternative tools, the context and phrasing provide strong usage 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 declare readOnlyHint=true, and the description aligns with a read-only query operation. The description adds behavioral context beyond annotations by specifying the API contract endpoint and listing both free and paid packs with pricing hints (x402 Base USDC). No contradictions.

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 plus a structured list of packs. The main purpose is front-loaded. The pack list could be considered verbose but is functionally beneficial. Every sentence adds value. Slightly longer than minimal but well-organized.

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 lacks details about return values, pagination, error handling, or response format, especially since no output schema is provided. It references an external API contract which may fill the gap but is not self-contained. For a query tool with complex parameters and no output schema, more completeness is expected.

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?

All 8 parameters have descriptions in the input schema (100% coverage), so the schema already documents each parameter. The description does not add individual parameter meaning, only a global note about available packs. Baseline 3 is appropriate as the description adds no further parameter semantics.

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: 'Generic structured query for direct source_id or pack_id access'. It lists available data packs, distinguishing it from sibling tools like get_catalog (catalog browsing) and get_pack (pack metadata). The verb 'query' combined with 'source_id or pack_id' makes the resource and action unambiguous.

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 listing free and paid packs, suggesting when to use this tool (to query those specific datasets). However, it does not explicitly state when not to use it or compare it to alternatives like search_disaster_links. The reference to 'same contract as POST /api/v1/query/dataset' provides some context but no 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?

With readOnlyHint=true already provided, the description adds minimal behavioral context beyond 'Free linked-disaster discovery helper.' It does not disclose matching behavior, result format, or error conditions.

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?

Three sentences with front-loaded purpose. The first sentence is somewhat vague ('Free linked-disaster discovery helper'), but overall efficient.

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 core functionality and when to use, but lacks return type information (no output schema). For a 7-parameter tool, it adequately explains the search, but missing output context is a notable gap.

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 described. The description summarizes the filtering dimensions (event-type direction, via, year window) but adds no new semantic detail beyond 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 tool searches for cross-disaster link families by event-type direction and optional filters, explicitly distinguishing it from siblings like get_catalog and query_dataset which 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 provides a clear use case: 'Use this when you want to discover whether a relationship family exists before you have an exact event id.' It does not explicitly mention when not to use or alternatives, but the guidance is sufficient.

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