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

Annotations already cover read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful context beyond annotations: the data source (NASA CMR), the public domain/no-auth nature, and a concrete list of metadata fields returned. 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?

The description is organized with a clear main purpose first, followed by return contents, a pointer to search results, use-case context, and source/auth notes. Each sentence adds value; it is slightly long but not bloated.

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 a single required parameter, an output schema, and rich annotations, the description provides enough context to call the tool correctly. It covers what is returned, where concept IDs come from, why the tool is useful, and auth requirements. Minor omissions like error conditions or rate limits are not critical for this simple lookup.

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% for the single parameter, so the schema already explains concept_id and gives an example. The description reinforces this by repeating the example and adding that concept IDs come from search results, which is helpful but not a significant addition 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 names a specific verb ('Retrieve'), a specific resource ('full UMM metadata for a specific NASA CMR collection'), and the identifier required (concept ID). It also differentiates from sibling operations by pointing to nasa-cmr.datasets.search as the source of concept IDs, making the tool distinct from search and granule operations.

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 clear situational guidance: use it to understand dataset scope, instrument provenance, and citation details before downloading granules. It also implicitly excludes granule-level queries by saying the tool is 'essential before downloading granules,' though it does not explicitly state when not to use it or name a specific alternative tool.

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