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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses significant behavioral traits: a subscriber API key and Consultant tier requirement, the 256 KiB response ceiling, the structure-first behavior, case-insensitive section matching, and inclusion of the introduction. This is rich context useful for an agent.

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?

Every sentence earns its place: purpose, auth constraint, size limitation, usage mechanism, and pointer to list_studies. The description is front-loaded with the core action and then methodically explains the chunked reading approach without redundancy. Length is justified by the tool's complexity.

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 (auth tier, size limit, section-based reading) and the presence of an output schema, the description fully covers how to invoke it correctly: what to pass, what to expect, and where to get inputs. No critical usage aspect 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 baseline is 3. The description adds value by noting that `section` matching is case-insensitive and that omitting `section` returns not just the section list but also the introduction. It also clarifies that `slug` is verbatim from list_studies, reinforcing schema hints.

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 opens with a specific verb+resource: 'Read one Analytics Legends study BODY — the paid text behind list_studies' metadata.' This clearly distinguishes it from sibling list_studies and other get_* tools by focusing on the full text body, not metadata or other entity types.

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?

It explicitly tells when to use this tool vs list_studies: 'Find slugs and languages with list_studies.' It also provides a full usage pattern: call without `section` for the section list and introduction, then pass a section heading for one section. No exclusions are needed because the alternative is named.

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