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

Annotations (readOnlyHint=true, idempotentHint=true) already cover safety. The description adds useful context: no auth required, the nature of granules (science data files like HDF, NetCDF, GeoTIFF), and the returned fields (IDs, titles, cloud cover, file size, download URLs). This goes beyond annotations without contradicting them.

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 three sentences, front-loaded with the core purpose and filter capabilities. It efficiently includes usage sequence, return fields, and source attribution without redundancy. Every sentence adds value.

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 presence of an output schema and rich parameter schemas, the description covers the essential usage flow, filter options, return fields, and authentication. It also mentions the prerequisite step (search datasets first). No critical information for an agent to call the tool correctly is missing.

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%, so the schema already documents all 7 parameters with detailed descriptions and examples. The description mentions filters like 'temporal range, spatial bounding box, and day/night acquisition flag' but does not add meaning beyond what the schema provides. It does clarify the alternative between collection_concept_id and short_name, but that's also in the schema. Thus, 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 states a specific verb ('Search'), a clear resource ('individual data granules (files) within a NASA CMR collection'), and lists filtering options (collection concept ID, short name, temporal, bbox, day/night). It explicitly contrasts with the sibling tool nasa-cmr.datasets.search, which finds collections, making the purpose 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 gives explicit usage guidance: 'Use nasa-cmr.datasets.search first to find the collection concept ID, then use this tool to locate specific files by time/area.' It also explains that short_name is an alternative to collection_concept_id, covering when to use which parameter.

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