DaedalMap UN Sustainable Development Goals

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 'Free discovery' and 'live agent-ready data packs,' which gives context about cost and readiness. It doesn't contradict annotations, and while it could mention more behavioral traits like response format, it compensates well given the 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 a single, efficient sentence that front-loads key information ('Free discovery') and states the core action and resource. There is no wasted text, making it easy for an agent to parse quickly and understand the tool's essence.

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 is complete enough for an agent to use it correctly. It covers purpose and context, though it could benefit from slight elaboration on output or usage scenarios to reach a perfect score, but it's adequate for the complexity.

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 coverage, the baseline is high. The description adds no parameter details, which is fine since there are none. It effectively communicates that no inputs are needed, aligning with the empty schema, so it meets expectations without redundancy.

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,' specifying the verb 'returns' and resource 'list of data packs.' It distinguishes from siblings like get_earthquake_events by focusing on catalog discovery rather than specific data types, though it could be more explicit about the distinction.

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 of available data packs, which suggests it's for initial exploration or listing resources. However, it lacks explicit guidance on when to use this versus alternatives like get_pack (for specific packs) or query_dataset (for querying data), leaving some ambiguity for the 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 indicate readOnlyHint=true; description adds context about what is returned (metadata, coverage, freshness, etc.) and implies no side effects. 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 concise sentences, front-loaded with 'Free discovery.' Every sentence adds value – purpose and usage guidance. No fluff.

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 one-parameter read tool without output schema, the description lists what the response contains (metadata, coverage, freshness, etc.), which is sufficient context 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?

Schema covers the single parameter fully with examples. Description adds no extra parameter meaning beyond what schema provides, meeting baseline for 100% 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 returns metadata, coverage, freshness, tool guidance, and examples for one pack. It distinguishes from siblings like get_catalog (list all packs) and query_dataset (query).

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 calling this before querying a new pack to inspect time shape, coverage limits, and get a paste-ready first query. Provides clear when-to-use 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 declare readOnlyHint=true, so the description's mention of a POST contract adds mild context (it may use a POST method). Does not disclose pagination behavior, rate limits, or response format. No contradiction 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?

Two concise sentences front-load the core purpose. Every phrase earns its place: the first defines the tool, the second lists actionable pack categories. 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?

With 8 parameters, no output schema, and complex nested objects, the description provides high-level purpose and pack examples but omits guidance on constructing filters, metrics, or interpreting responses. Adequate but incomplete 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 coverage is 100% (all 8 parameters have descriptions). The description adds value by listing free vs paid packs, which aids pack_id selection. However, it does not elaborate on complex parameters like filters or metrics beyond what schema provides.

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 clearly defines a verb+resource: 'Generic structured query for direct source_id or pack_id access'. It distinguishes from siblings (get_catalog, get_pack) by specifying it's for direct access to dataset endpoints. Lists specific packs, adding precision.

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 states 'using the same contract as POST /api/v1/query/dataset' and distinguishes free vs paid packs, which helps decide which pack_id to use. Lacks explicit when-not-to-use guidance or alternatives, but the pack categorization provides clear context.

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