DaedalMap CIA World Factbook

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 'live agent-ready data packs' and 'Free discovery,' which clarifies the tool's scope and accessibility. However, it doesn't disclose behavioral traits like rate limits, authentication needs, or response format details 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 extremely concise at just 9 words, front-loaded with the key action ('Returns the list...'). Every word earns its place by specifying the resource type, availability status ('live agent-ready'), and source ('on DaedalMap'). There's zero waste or 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 (0 parameters, read-only, no output schema), the description provides adequate context about what it returns. It could be more complete by mentioning the return format (e.g., list structure) or any limitations, but for a basic discovery tool with good annotations, it's sufficiently informative without being verbose.

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 lack of inputs. The description appropriately doesn't add parameter information, as none are needed. This meets the baseline expectation for a parameterless tool, earning a 4 for not introducing unnecessary complexity.

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 'Returns the list of live agent-ready data packs available on DaedalMap' - a specific verb ('Returns') and resource ('list of live agent-ready data packs'). 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 individual packs.

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 'Free discovery' and 'live agent-ready data packs,' suggesting this is for browsing available resources. However, it provides no explicit guidance on when to use this versus alternatives like 'query_dataset' for specific queries or 'get_pack' for individual pack details. The context is clear but lacks sibling 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 provide readOnlyHint=true, indicating a safe read operation. The description adds valuable context beyond this: it specifies that this is for 'Free discovery' (implying no cost or authentication barriers) and describes the return content (metadata, coverage, metrics, guidance). It doesn't mention rate limits or error conditions, but with annotations covering safety, the added context justifies a strong score.

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 key information ('Free discovery. Returns detailed...'). Every word earns its place by specifying the action, output components, and target resource without redundancy or fluff. It's efficient and immediately informative.

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 required parameter), high schema coverage (100%), and annotations (readOnlyHint), the description is largely complete. It clearly states the purpose and output components. The main gap is the lack of an output schema, but the description compensates by listing return types (metadata, coverage, etc.). For a read-only tool, this is sufficient though not exhaustive.

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 pack_id parameter fully documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., no examples of valid pack_ids beyond those listed in the schema). Baseline 3 is appropriate as the schema carries the full burden of parameter documentation.

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 ('Free discovery. 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 dataset-specific tools like get_earthquake_events. The verb 'Returns' combined with the detailed output components makes the purpose explicit and differentiated.

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 initial exploration of a pack. However, it doesn't explicitly state when to use this versus alternatives like get_catalog (for browsing all packs) or query_dataset (for actual data queries). The guidance is present but not fully explicit about alternatives 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 provides readOnlyHint=true, which the description doesn't contradict. The description adds valuable context about free vs. paid packs (including pricing for tsunamis) and references the API contract, which helps the agent understand access constraints. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, or what happens with invalid queries 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 appropriately concise with two sentences that each serve a purpose: the first defines the tool's function and API contract, the second provides pack examples with pricing context. There's no wasted verbiage, though it could be slightly more structured for readability.

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 rich schema coverage, the description provides adequate context about the query nature and pack access. However, it lacks details on response format, error handling, or how this generic tool relates to the more specific sibling tools, leaving some gaps for an agent to fully understand usage.

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 schema already documents all 8 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond mentioning pack examples (which partially overlaps with pack_id schema description). This meets the baseline of 3 when schema coverage is high.

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 a specific API endpoint, which provides a specific verb ('query') and resource ('dataset'). However, it doesn't explicitly differentiate this generic query tool from its more specific siblings like 'get_earthquake_events' or 'get_fx_rates' beyond mentioning pack examples.

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 some usage context by listing free vs. paid packs and mentioning the API contract, which implies this is the primary query interface. However, it doesn't explicitly state when to use this tool versus the more specific sibling tools (like get_earthquake_events vs. querying earthquakes via pack_id), nor does it mention any prerequisites or exclusions for usage.

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