RoRo Quantum

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds genuine behavioral value beyond that by disclosing the nature of the response — an overview with links and the named product areas — which is the key unknown for a zero-parameter tool.

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?

A single front-loaded sentence with no waste; the enumeration of covered topics earns its place by telling the agent what the response contains.

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 burden of explaining the return payload, and it does so at a useful level (overview plus links to five named areas). It stops short of describing format or size, but for a static informational tool this is close to complete.

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?

The tool takes zero parameters, so per the baseline this is a 4. The description correctly implies no input is required by simply describing what the tool returns.

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 resource (RoRo Quantum's overview) and enumerates exactly what it covers: Academy, Console, Lab, RoRo Copilot, and the developer API. This is instantly distinguishable from the sibling retrieval tools (get_lesson, list_learning_tracks, search_lessons), which fetch concrete items rather than an orientation summary.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternative is named. Usage is only implied: an agent can infer this is the orientation/first-call tool rather than a lookup tool, which is adequate but not stated.

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 read-only, idempotent, non-destructive behavior, so the bar is lower. The description still adds genuine context: the return payload is text with a link, and the notable constraint 'Quiz answers are never included' — a behavioral boundary an agent could not infer from 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?

A single sentence, front-loaded with the verb and resource, followed by the returned-content clause and the quiz-answer caveat. No filler or redundancy.

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 simple read-only tool with full schema coverage, low complexity, and no output schema, the description plus annotations give an agent enough to call it correctly. It could mention pagination or lesson-size limits, but 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?

Schema description coverage is 100%, so both parameters (slug and the language enum) are fully documented in the schema. The description only echoes slug-based lookup and adds no syntax, format, or default detail beyond it; baseline 3 is correct.

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 and resource ('Read one RoRo Academy lesson by its slug') and enumerates the returned content (explanations, formulas, example circuits, an Academy link). This distinguishes it cleanly from search_lessons and list_learning_tracks, which don't fetch a single lesson by identifier.

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 implies usage ('Read one ... by its slug') but never states when to prefer this over search_lessons or what to do when a slug is unknown; that routing hint ('as search_lessons returns it') lives in the schema, not the description. Adequate implied context, no explicit alternatives or exclusions.

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/non-destructive, so the safety profile is covered. The description adds that it returns tracks with their modules and lesson counts, which is useful scoping, but no pagination, ordering, or other behavioral detail is given.

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?

A single front-loaded sentence with no filler. The trailing enumeration of track names is slightly verbose but does add concrete value by naming the content returned.

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-required-parameter, read-only list tool with no output schema, the description adequately conveys what comes back and what the tool covers. Minor gaps around ordering/pagination are not critical here.

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 optional parameter (language) and the schema documents it at 100% coverage, including enum values and defaulting guidance. The description adds nothing about the parameter, so the schema-carries-it baseline of 3 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 resource (RoRo Academy's learning tracks) and even enumerates the tracks returned. It does not explicitly distinguish itself from siblings like search_lessons or get_lesson, so it falls short of a 5.

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?

No indication of when to use this over search_lessons or get_lesson, and no mention of prerequisites. Usage is only implied by the tool being a catalog-style list.

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=true, idempotentHint=true, destructiveHint=false and a closed world, so the safety profile is fully covered. The description adds the scope of the listing (hardware plus simulators) and the returned fields, but no auth, rate-limit, or pagination behavior beyond that.

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?

A single front-loaded sentence with no filler; the resource leads and the returned field list follows. Every clause carries information the agent can use.

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 helpfully enumerates the fields returned, and the annotations carry the safety profile, so an agent has enough to call it correctly. It is slightly thin on when to prefer this over the sibling tools, but nothing essential is missing for a read-only listing.

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% and the single 'kind' enum is fully documented in the schema itself (all/simulator/hardware). The description only echoes 'simulators' implicitly and adds no syntax or default detail, so the baseline 3 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?

Specific verb ('List') plus resource ('quantum computers and simulators') with the returned attributes enumerated (name, id, qubit count, technology, status). It is unambiguously distinct from the learning-oriented siblings, so an agent can select it without opening the 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?

The description states what the tool returns but never says when to call it, what the filter is for, or what alternatives exist. Usage is only inferable from the tool name; there is no explicit context or exclusion.

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 this is a read-only, idempotent, non-destructive operation, so the safety burden is light. The description still adds value beyond them by disclosing the returned fields (titles, summaries, level, length, links) and the empty-query behavior, though it omits any note on result ordering semantics when a query is present.

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?

Three tight sentences, front-loaded with the core purpose, then return values, then the edge-case behavior. No redundancy and nothing that could be trimmed without losing information.

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 usefully enumerates the returned fields, and it covers the empty-query case. It stops short of describing ordering or truncation behavior when results exceed the limit, which is a minor gap for a search tool.

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%, so the schema already documents query, limit, and language, including the enum meanings and defaults. The description adds topical query examples and the empty-query semantics for 'query', but says nothing further about limit or language behavior, so the baseline of 3 holds.

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 and resource (find quantum computing lessons by topic) and names the domain (RoRo Academy), with concrete topic examples that make the retrieval target unambiguous. It does not, however, explicitly contrast itself with siblings like get_lesson or list_learning_tracks, so differentiation is inferred rather than stated.

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 implies the usage context (topic search) and gives one useful edge case: an empty query lists lessons in course order. It never names an alternative sibling or states when to prefer this over get_lesson or list_learning_tracks, so the routing guidance is only implied.

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