DaedalMap Volcanic Activity

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

Annotations declare readOnlyHint=true, indicating a safe read operation. The description adds value by specifying that it returns 'live agent-ready data packs' and implies a discovery context with 'Free discovery', but doesn't detail behavioral aspects like rate limits, authentication needs, or response format. No contradiction with annotations exists, so it meets the baseline for tools with annotations.

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: two short sentences with zero waste. 'Free discovery' sets context efficiently, and the second sentence clearly states the action and resource. Every word earns its place, making it easy for an AI agent 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?

Given the tool's simplicity (0 parameters, read-only annotation, no output schema), the description is adequate but has gaps. It explains what the tool does but doesn't cover usage guidelines or differentiate from siblings, which could help in tool selection. For a list-retrieval tool, it's minimally viable but lacks completeness in contextual guidance.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose. This aligns with the baseline score of 4 for zero-parameter tools, as it avoids unnecessary 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: 'Returns the list of live agent-ready data packs available on DaedalMap.' It uses a specific verb ('Returns') and resource ('list of live agent-ready data packs'), though it doesn't explicitly differentiate from sibling tools like 'get_pack' or 'query_dataset'. The 'Free discovery' phrase adds context but isn't essential to the core purpose.

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 doesn't mention sibling tools like 'get_pack' (which might retrieve a specific pack) or 'query_dataset' (which might search datasets), nor does it specify prerequisites or exclusions. The 'Free discovery' phrase hints at exploratory use but lacks explicit usage 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?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds context by specifying it's a 'free live wrapper' and that data is 'normalized to DaedalMap event fields', which helps understand the source and format. However, it lacks details on rate limits, authentication needs, or error handling, which would enhance transparency further.

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: the first explains the core functionality and source, and the second clarifies the data scope. Every sentence adds value without redundancy, making it efficient and front-loaded.

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) and annotations (readOnlyHint), the description is reasonably complete. It covers purpose, source, and data normalization, but could benefit from more behavioral details like response format or limitations, though annotations help mitigate 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?

Schema description coverage is 100%, so parameters are well-documented in the schema. The description does not add specific parameter details beyond implying temporal filtering ('recent') and data scope ('preliminary'). This meets the baseline of 3, as the schema carries the primary burden.

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 specifies both the verb (calls/gets) and resource (volcanic eruption updates). It distinguishes from siblings by mentioning 'live' and 'preliminary' versus 'canonical history lane', though not explicitly naming alternatives like 'get_volcanic_activity'.

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 'recent preliminary volcanic eruption updates' and contrasts with 'enriched canonical history lane', suggesting when to use this tool (for live/preliminary data) versus alternatives (for historical/canonical data). However, it does not explicitly name sibling tools or provide clear exclusions, 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 already declare readOnlyHint=true, so the description's addition of 'Free discovery' and details about returned data adds context beyond the annotation. It discloses no destructive or side effects, consistent with read-only. The description enriches understanding without contradicting annotations.

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, front-loaded with 'Free discovery'. Every sentence is informative and necessary, with no wasted words. It is concise yet complete for 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 low complexity (single parameter, read-only, no output schema), the description provides sufficient context: what it returns, when to use it, and its purpose. It covers all essential aspects without needing additional elaboration.

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%, and the schema already provides a clear list of valid pack_id values. The description does not add extra meaning or constraints beyond what the schema offers, so it meets the baseline for 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 verb 'returns' and the resource 'detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack'. It distinguishes itself from siblings by specifying it is for a single pack and should be called before querying a new pack, unlike get_catalog or live event tools.

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 advises 'Call this before querying a new pack', providing clear when-to-use guidance. While it does not explicitly list when not to use or alternative tools, the context implies it is for initial discovery, and sibling tools are named for differentiation.

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 have readOnlyHint=true, and the description is consistent, adding useful context about query behavior (e.g., year-style filters for time ranges). 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?

Two sentences only: the first states the purpose and the second gives usage guidance. No fluff, well front-loaded, every sentence earns its place.

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 does not explain return values. However, it covers purpose, usage, and parameter nuances. With 6 parameters and nested objects, it is sufficiently complete for an experienced agent, though a brief note on typical response structure would be helpful.

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 descriptions for all 6 parameters. The description adds valuable guidance for the 'limit' parameter (include narrow year range for top-N requests) and reinforces the use of year-based time filters for 'filters'.

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 queries volcanoes_events for historical eruption records and volcanic activity metrics, with specific use cases like eruption counts and VEI thresholds. It implicitly distinguishes from siblings like get_live_volcano_events by focusing on 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 recommends use for eruption counts, VEI thresholds, and top-event lookups, and notes that year-style time filters are typical. It does not explicitly state when not to use or name alternatives, but the context is helpful.

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