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

Goes well beyond the readOnly/idempotent annotations: it discloses that source fetches require a signed-in account, count against a daily install cap, that Pro effects need a subscription, and that locked/rate-limited responses are expected rather than errors ('Don't retry'). It also names the sentinel response fields (sign_in_required, source_locked, rate_limited, install_remaining) so the agent can interpret outcomes.

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 purpose sentence and organized under clear Args/Returns/Examples/Error Handling headers, so it scans well. However, the sign-in and rate-limit explanation is restated across three paragraphs and duplicates part of the schema's include_source description, so it runs longer than strictly necessary.

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?

There is no output schema, and the description fully documents the JSON return shape including nested changelog entries and conditional files[].content. Auth, quota, Pro gating, and the not-found error are all covered, so an agent has everything needed to call and interpret it.

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 would be 3, but the description adds real meaning: include_source's default of false, the quota cost implication, and the auth gating, plus a worked example for each value. Marginal redundancy with the schema's own include_source description keeps it from a 5.

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?

Opens with a specific verb and resource ('Get full details for one Hyperiux Vault effect by its exact slug') and enumerates exactly what is returned. It is clearly differentiated from hyperiux_list_effects, which is named both as the alternative and in the error-handling section.

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 when to use it (you have an exact slug), when not to ('Don't use when: you don't have an exact slug yet - use hyperiux_list_effects first'), and gives three concrete usage examples including the include_source=true variant. The alternative and the condition selecting it are both named.

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 readOnly, idempotent, non-destructive, and openWorld, so the safety profile is covered. The description adds non-redundant behavioral details: the exact JSON return format, the sort order (count descending), and that it takes no arguments. 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?

Every sentence earns its place: purpose, return shape, sort order, usage example, and a clear don't-use-when rule. The structure front-loads the core behavior and uses compact examples instead of verbose prose.

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 zero-parameter, read-only listing tool with no output schema, the description fully covers what an agent needs: what it returns, in what order, when to call it, and when to choose a sibling instead. Nothing essential 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?

There are zero parameters, so the schema and context signals already make parameter usage trivial. The description still explicitly confirms invocation with no args, aligning with the baseline for param-free tools.

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'), a clear resource ('every effect category in the Hyperiux Vault catalog'), and the key detail of per-category effect counts. The differentiation from siblings is explicit by focusing on categories as distinct from effects and individual effect retrieval.

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?

Provides explicit guidance: use before hyperiux_list_effects with a category filter to confirm slugs and sizes, and explicitly says not to use it when the slug is already known and effects are wanted. It names the exact alternative tool and the condition under which to switch.

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, idempotentHint and openWorldHint, so the safety profile is covered; the description adds the return payload shape (total/count/has_more/next_offset) and pagination behavior, which is valuable since no output schema exists. It stops short of noting catalog freshness or rate limits, so it is strong but not exhaustive.

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 purpose followed by labeled Args/Returns/Examples blocks, which is easy for an agent to parse. Slightly verbose in the repeated warnings about not calling hyperiux_get_effect to count, but each block is functional.

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 read-only list tool with no output schema, the description supplies the return field list, pagination semantics, and the tier-based counting caveat an agent needs to answer aggregate questions correctly. Nothing required to call it or interpret results 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 baseline is 3, but the description goes beyond it by pairing each parameter with a real intent example (category='cursor', query='particles') and explaining the limit/has_more interaction needed for counting. It does not restate defaults or bounds already in 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?

Opens with a specific verb+resource pair ('Browse or search the Hyperiux Vault catalog of React/Next.js interaction effects') and enumerates the effect types covered. It clearly separates this list/search operation from hyperiux_get_effect, so an agent can route without reading schemas.

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?

Provides explicit routing guidance: use this for aggregate counts and tier filtering, do not call hyperiux_get_effect per-item to total them, and 'Don't use when' names the sibling that handles single-effect detail. The three intent-to-argument examples make the mapping concrete.

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