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

The description adds valuable behavioral detail beyond the annotations: it states the tool publishes to the learner's app, returns a deep link, and specifically discloses that the notes field is never shown to the learner. This gives the agent solid expectations for side effects and privacy, complementing the readOnlyHint=false and destructiveHint=false 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?

Three sentences with no redundancy: it states the action, the return, then two pieces of practical guidance. Information is front-loaded (action first), and each sentence earns its place without fluff.

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 10-parameter creation tool with a rich schema and output schema present, the description covers the core purpose, return behavior, best-practice usage, and a privacy nuance. Nothing essential is missing for an agent to invoke it correctly; the schema handles parameter details.

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% and every parameter has a description. The description does not introduce new parameter meaning beyond restating the notes privacy and the card-count advice already present in the schema. It adds no new semantics that the schema doesn't already provide, so the baseline 3 is appropriate.

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 clearly states the action ('Build a flashcard set and publish it') and the resource (flashcard set in Vocabit). It also mentions the return value (deep link), distinguishing it from list/get/update/delete siblings. The verb is specific and the purpose is unambiguous.

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 provides strong best-practice context ('Prefer one focused topic and 8-15 cards per set') but does not explicitly contrast with alternatives like update_study_set or when not to use it. It gives clear context for using the tool effectively but lacks an explicit 'use X instead' routing.

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 indicate destructive and non-read-only. The description adds context by specifying that study history persists on the backend while the device copy is removed, and instructs to seek permission. This goes beyond the annotation flags, giving the agent a precise understanding of side effects.

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 short sentences, each adding distinct value: purpose, scope nuance, and usage directive. 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 single-parameter destructive operation with an output schema available, the description covers purpose, scope, and a user-consent requirement. Nothing critical 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?

The schema fully documents the sole parameter (setId) with a source reference, and the description adds no additional parameter semantics. With 100% schema coverage, 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?

The description clearly states the action (remove) and resource (a set from the learner's app), with further scope clarification (backend history kept, device set removed). It does not explicitly name sibling tools, but the verb distinguishes it from create/update/list, making its purpose unambiguous.

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 an explicit directive 'Ask before calling this' and clear context of what removal entails. It lacks explicit exclusions or alternatives, but the usage context is obvious for a deletion task and the consent instruction is a strong behavioral guideline.

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 provide readOnlyHint=true and openWorldHint=true, so the description doesn't need to restate safety. It adds value by explaining what the read returns (weakCards, untouchedCards, review counts) and how to use it, which goes beyond the schema. No contradiction 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?

Two sentences, zero fluff. The purpose is front-loaded, and the usage guidance is packed into the second sentence. Every word earns its place.

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?

Since an output schema exists, return values are covered. The description gives the loop context and the key fields, which is sufficient for an agent to select and call correctly. Minor gaps like pagination or error handling are not critical for a simple read 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 both setId and userId are already documented. The description adds no parameter-level detail beyond what the schema provides, so it doesn't compensate further, but it also doesn't detract. 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?

The description uses a specific verb ('Read back') and identifies the resource ('real study results for a set') with concrete fields (weakCards, untouchedCards, per-card review counts). It clearly distinguishes itself from siblings like get_study_set by focusing on performance results, and explicitly frames this as the 'feedback half of the loop'.

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 explicit usage guidance: 'read it before writing the next set' and advises building follow-up from weakCards. This tells the agent exactly when to use this tool and how to act on its output, which is more explicit than most tool definitions.

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, so the description doesn't need to repeat that it's a read operation. The description adds valuable context beyond the annotation by specifying the exact content returned: 'every card, plus the topic and notes you attached when you created it.' This enriches the agent's understanding of what the tool provides without contradicting any annotation.

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 a single, front-loaded sentence that starts with the verb 'Read,' immediately conveying the action. There is no fluff or redundancy. It efficiently conveys the scope of the tool in a way that is easy to scan, earning a full score for conciseness and structure.

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 that an output schema exists (has_output_schema=true) and the single parameter is documented in the schema, the description need not explain return formats or parameter formats. The description covers the necessary contextual information—what data is returned (all cards, topic, notes)—which is sufficient for an agent to call the tool correctly. There is no missing information that would impede correct usage.

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%; the parameter setId has a clear description ('Set id returned by create_study_set or list_study_sets'). The tool description adds no additional information about the parameter—it doesn't mention setId at all. Since the schema fully documents the parameter, the description does not need to compensate, so baseline 3 is appropriate.

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 clearly states a specific verb ('Read') and resource ('full contents of a set'), and specifies exactly what is included (every card, topic, notes). This distinguishes it from sibling tools like list_study_sets (which likely provides summary metadata) and get_set_results (which likely returns performance data). The agent can immediately understand what the tool does 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 implies this is the tool to use when you need the full contents of a study set, but it does not explicitly mention when not to use it or name alternatives. For example, it doesn't say 'use list_study_sets for a summary' or 'use get_set_results for results.' There is no explicit guidance on choosing among the related collection/read tools, leaving the agent to infer when this one is appropriate.

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 and openWorldHint, so read-only safety is covered. The description adds value beyond that by disclosing the scope (sets created for this learner), the newest-first ordering, and that each set carries a progress summary showing whether it was opened. No contradiction with the 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?

Two sentences with zero waste. The core purpose is front-loaded, and the second sentence earns its place by adding the two primary use cases. Nothing extraneous.

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 an output schema present, read-only/open-world annotations, and 100% schema coverage, the description doesn't need to explain return values or parameters. It covers purpose, scope, ordering, and use cases well. The only minor gap is that it doesn't explicitly route users to the get_study_set/get_set_results siblings for more detail, and it omits pagination specifics — both minor for a list 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 all four parameters (limit, topic, userId, includeProgress) are already well-documented in the schema itself. Under the rubric, this yields a baseline of 3; the description adds no parameter-specific detail beyond what the schema provides.

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 verb (list), resource (study sets), scope (created for this learner), ordering (newest first), and content (progress summary per set). It also frames concrete use cases — finding a setId and spotting never-opened sets — which make its purpose unmistakable and distinct from the singular get_study_set and get_set_results siblings.

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 explicit when-to-use guidance ('Use it to find a setId, or to see at a glance which sets were never opened'). However, it does not name alternatives or state when not to use it, such as pointing to get_set_results for detailed results, so it falls short of a 5.

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 flag non-read-only, non-idempotent behavior; the description adds the 'use sparingly / one ping' rule, which reinforces the side-effectful nature beyond what annotations state. No contradiction.

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?

Two concise sentences with the purpose first and the guidance second. No filler.

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?

A simple notification tool with an output schema; the description covers what it does and when to call it. Nothing critical 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?

Both parameters are fully described in the schema (100% coverage), and the description adds no additional parameter semantics, so it meets the baseline.

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 action (send a Telegram message) and the resource (learner about a set). Clearly distinguishes from the CRUD siblings—it's a notification tool, not a data operation.

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 instructs sparing use and the exact timing ('right after you create it'), which tells the agent when to invoke it. Lacks explicit 'when not to use' or alternatives, but the constraint is clear.

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 signal mutability (readOnlyHint=false) and non-destructiveness (destructiveHint=false). The description adds a valuable side effect beyond annotations: 'Replacing the cards resets what the learner has already studied,' which is a behavioral consequence not captured in structured fields. It also warns against combining addCards and cards, adding safety context.

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?

Two tight sentences carry all essential information: the action, the two operational modes, the constraint, and a key side effect. No filler, front-loaded with purpose, and every phrase earns its place.

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 mutation tool with 8 parameters, the description provides enough to call it correctly: it explains the main operational choices, the safety constraint, and the meaningful side effect. The output schema covers return details, and remaining parameters are straightforward. Nothing an agent needs 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 only 38%, so the description must compensate. It does so by explaining the two most ambiguous parameters—addCards (append) and cards (replace)—and their mutual exclusivity, plus the learning-reset consequence. Other parameters (title, topic, visibility) are self-explanatory, and the critical ones are well addressed, bridging the coverage gap.

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 action ('Change a set in place') and lists concrete operations (retitle, retag, add cards), clearly distinguishing it from create/delete/list siblings. The verb 'change' and resource 'study set' are explicit, and the added detail on cards vs. addCards further sharpens the purpose.

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 on choosing between addCards (append, 'the usual case after reviewing results') and cards (replace wholesale), including the 'never both' constraint. It implies when to use the tool (modifying an existing set) but does not directly contrast with create_study_set for new sets; however, the parameter-level usage is well covered.

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 and openWorldHint, so the description needn't restate safety. It adds value by disclosing that the tool reports the operational mode (live vs demo), which is beyond the annotations. It doesn't elaborate on failure behavior, but the output schema likely covers that, and the description is sufficient for a health check.

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?

Two tight sentences: the first states purpose and behavior, the second gives usage guidance. No filler, and the most important information (what it does) is front-loaded.

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 health check with an output schema, the description is complete. It states what it verifies, what it reports, and when to call it. The output schema handles return-value details, so 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?

With zero parameters and 100% schema coverage, there is nothing for the description to explain. The baseline for 0 params is 4, and the description appropriately does not invent unnecessary parameter details.

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 clear verb 'Verify' and resource 'Vocabit', and specifies the output (mode: live or in-memory demo). It is immediately distinct from siblings, which all handle study sets and notifications, so an agent can easily tell this is a health-check tool.

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 guidance is given: 'Call this first if anything else fails.' This provides a concrete trigger condition and implies it's a diagnostic first step. No ambiguity about when to use it.

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