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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely useful context beyond them: the field-level shape of each dataset (id, kind, target domain, market, status, aggregate-vs-individual totals) and the 'unavailable means no research yet' semantic. It does not mention pagination, rate limits, or auth requirements, so it falls short of a 5.

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?

Front-loaded with the core purpose, followed by routing rules, then edge-case semantics. The middle sentence enumerating returned fields is long but earns its place given there is no output schema; only minor tightening is possible.

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 no output schema, the description carries the return-value burden and does so by naming the dataset fields and the project-level extras (owned targets, tracked competitors, latest run). Combined with the routing guidance, an agent has nearly everything needed; only pagination/size expectations are unaddressed.

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?

Only one parameter with 100% schema description coverage, and the schema ties it to list_projects' output. The description adds no further syntax or format meaning for project_id, so the baseline of 3 for schema-covered params applies.

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?

States a specific verb ('List') and a precise resource ('keyword, competitor and backlink research datasets saved for this project'), and immediately distinguishes itself from read_search_dataset ('It lists datasets only') and read_query_evidence. An agent can route between these three siblings without opening any schema.

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?

Explicit when-to-use and when-not-to: pass a dataset id to read_search_dataset for rows, use read_query_evidence for Search Console queries, and 'this tool never starts research, which happens in CiteLadder'. It also explains the 'unavailable with no datasets' status as a signal that no research was run yet.

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