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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose atomicity ('原子地'/'Atomically') and enumerates the operation types (update/delete/disable/archive/create), which is meaningful. But it does not disclose the consequences of destructive ops (e.g., whether delete is irreversible or archive is restorable), error handling on partial failure, or response behavior—significant gaps for a mutation tool with zero annotation coverage.

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 compact but redundant: the first Chinese clause ('原子地执行用户已确认的整理操作清单') and the final English sentence ('Atomically apply user-approved consolidation operations') convey the same message twice, which wastes space. The op-semantics clause earns its place, but the bilingual duplication could be trimmed to a single canonical sentence.

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 batch mutation tool with five operation types, no output schema, and no annotations, the description covers purpose, atomicity, and op semantics but omits what happens on success or failure—particularly since the atomic guarantee makes rollback behavior relevant. A note on return values or error semantics would substantially improve completeness, though the essential usage contract is present.

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 0%, so the description must compensate, and it does meaningfully. 'op: update（带 fields）/ delete / disable / archive（可带 note 注明去向）/ create（带 fields）' maps each op enum value to its associated parameters, clarifying that update and create require fields and archive accepts a note for destination. The id parameter and the structure of fields are left unexplained, but the op-to-parameter mapping adds real value beyond the bare 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?

The description states a specific verb and resource: 'Atomically apply user-approved consolidation operations' / '原子地执行用户已确认的整理操作清单' (atomically execute the user-confirmed consolidation operations list). The 'user-approved' qualifier hints at a distinction from the sibling start_consolidation, but it never names the sibling explicitly, so the differentiation is implicit 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 phrase '仅限用户主导的管理场景' (only for user-led management scenarios) plus '用户已确认' (user-confirmed) provides clear context about when the tool is appropriate: after the user has approved the operations. However, it does not explicitly name alternatives such as start_consolidation or state when-not-to-use conditions beyond the 'user-led only' constraint.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly warns that the action is permanent and requires explicit user confirmation first, which covers the most critical safety aspects of a destructive operation. It does not detail side effects or error behavior, but for a simple one-parameter delete this is acceptable.

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 short and front-loaded with the core action and safety requirement. The bilingual repetition is redundant but still compact, and the key usage guidance is easy to parse. It earns a high score for efficiency, with minor points off for duplicated content.

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 low complexity of a one-parameter delete with no output schema, the description covers the essential context: what the tool does, when to use it, and the critical confirmation requirement. It is complete enough for an agent to invoke it correctly, though it omits details about response or failure handling.

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 has 0% description coverage for the only parameter, id, so the description must compensate. It provides context by identifying the target as a '预设/preset', allowing the agent to infer that id identifies the preset to delete. However, it does not explicitly document the id parameter or any constraints, so compensation is only partial.

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 action ('永久删除/Permanently delete') on a specific resource ('预设/preset') and distinguishes itself from mere updates by emphasizing permanence. It also implicitly separates this tool from update_memory by mentioning disabling as an alternative.

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 and when-not-to-use guidance: permanent deletion only after user confirmation, and temporary disuse should be handled by setting status=disabled. This is actionable and points toward the alternative behavior without ambiguity.

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?

With no annotations, the description carries the full behavioral burden. It communicates a non-mutating list/search action and notes that all filters are optional, but it does not state the default result when no filters are given, the scope of the search, or output/pagination behavior.

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 short and starts with the core action, but the Chinese and English halves largely restate each other ('列出或搜索已保存的预设' vs 'List/search saved presets'). It is minimal and not overly verbose, yet the duplication slightly undermines conciseness.

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 tool with five optional parameters, no annotations, and no output schema, the description is too thin. It does not clarify how memories relate to presets, what each filter selects, or what the tool returns, leaving an agent with incomplete context for invocation.

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 20%: only query is documented in the schema. The description adds only 'all filters optional' and does not explain project, category, task_type, or status semantics, so it fails to compensate for the low schema coverage.

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 and resource: 'list/search saved presets,' which clearly names the operation. It is weakened by a terminological mismatch: the tool name and title say 'memories' (记忆) while the description says 'presets' (预设), and it does not explicitly contrast itself with siblings like recall_presets or memory_overview.

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 phrase 'all filter conditions are optional' gives clear calling context (no required parameters), but there is no guidance on when to choose this tool over recall_presets or memory_overview. Usage is implied rather than explicitly scoped.

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?

No annotations are present, so the description carries the behavioral burden. It implies a read-only diagnostic via 'Overview with health checks' and enumerates returned metrics, but it never explicitly states that the tool has no side effects or will not mutate the memory store.

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 compact and front-loaded: the core statistics/health-check purpose comes first, followed by a clear invocation trigger and a concise English summary. Every part 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 zero-parameter overview tool with no output schema, the description is complete: it provides the trigger phrase, states the tool's scope, and enumerates the returned health-check categories. An agent has what it needs to invoke and interpret the call.

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 has zero parameters and the schema is already 100% covered by an empty properties object. There is no parameter information for the description to add, so the 0-parameter baseline of 4 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 identifies a diagnostic overview of the memory store: '统计 + 体检指标' and lists the health dimensions (suspect duplicates, stale candidates, graduated candidate clusters, pending confirmation, last organization time). This distinguishes it from list_memories and other sibling tools, making the resource and 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?

It explicitly sets the trigger condition: '用户说"看看我的记忆"时先调这个', so an agent knows when to call it first. It does not state negative conditions or compare to siblings by name, but the provided context is enough.

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?

With no annotations provided, the description carries the full burden and does so well: it discloses that the server may pop a confirmation dialog, enumerates the terminal states, and mandates verbatim relay of user_notice. This gives an agent clear behavioral expectations beyond the schema.

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 front-loaded with the core call timing and purpose, then lists terminal states and follow-up actions efficiently. The English summary at the end repeats the Chinese content, adding slight redundancy, but there is 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?

Despite having no output schema, the description enumerates the return terminal states and their required actions, which is nearly complete. It does not specify the exact JSON response shape beyond those states, but for a tool with three optional parameters this is a strong definition.

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 parameter descriptions already explain mode, project, and task_type. The tool description itself adds no parameter-specific detail beyond the schema, so the 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?

The description opens with a specific verb and resource: '开始任务时调用，召回适用于当前上下文的预设并完成确认'. It clearly distinguishes what the tool does from the sibling memory-management tools, and the terminal-state list plus the record_usage handoff further clarify its role.

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 states when to call it ('开始任务时调用') and gives handling guidance for each terminal state, including reporting pending_confirmation results via record_usage. It also distinguishes interactive vs unattended mode, but it does not explicitly say when not to use it or contrast it with list_memories/memory_overview.

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?

With no annotations, the description must carry the full burden. It explains three enum values (confirmed, rejected, overridden) but omits unattended_applied and unattended_skipped, which are in the schema. It also fails to disclose side effects, required permissions, or response behavior, leaving significant behavioral gaps.

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 concise, front-loading the purpose and defining key result values. It mixes Chinese and English, but that is not a major flaw. It could be slightly more structured, but it is appropriately sized.

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?

The tool has one parameter, no output schema, and no annotations. The description leaves out two enum values and domain-specific terms (pending_confirmation, backlog, correction flow) without explanation. It also does not cover when to use it relative to alternatives, making it incomplete for an agent to invoke correctly.

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 baseline is 3. The description adds value by explaining the meaning of three result values and the context of pending_confirmation, but it does not explain the two unattended enum values, leaving the parameter semantics incomplete.

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 tool reports per-preset consent outcomes resolved in conversation, with a specific verb ('report') and resource. The Chinese text adds precision by specifying '预设确认结果' (preset confirmation results) and enumerating three result semantics, making it distinct from sibling memory-management tools.

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 clear context: it is used when reporting confirmation results from conversation, including pending_confirmation replies, backlog re-confirmation, and overrides. It does not explicitly name alternatives or exclusions, but the context is unambiguous given the sibling tools' focus on memory operations.

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?

No annotations are provided, so the description carries the full burden. It discloses important traits: the dialog channel triggers another server-side confirmation, timeout means no save, and the tool returns potential_conflicts while passing through user_notice verbatim. It also clarifies that semantic conflicts should be handled by proposing an update to the old preset rather than creating a new one.

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 front-loads the core purpose and the required precondition, then adds output semantics in compact clauses. The final English sentence is slightly redundant with the opening Chinese sentence, but it does not bloat the description. Overall it is appropriately sized and structured.

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?

Because there is no output schema, the description explicitly explains the return value potential_conflicts and the user_notice passthrough, which an agent needs to decide how to handle conflicts. It also covers timeout/no-save behavior and the single-instruction exclusion. For a 7-parameter tool with no annotations, this is sufficient to invoke it correctly.

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 adds meaning to content and category by requiring that they be the refined expression and suggested category shown to and agreed by the user. It also reinforces how consent and risk/confirmation policies interlock. This is genuine extra semantics beyond the schema's field descriptions.

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 concrete action—"把用户确认过的可复用要求保存为预设"—and clearly identifies the resource as a user-confirmed durable requirement. It also adds a scope exclusion: single-use instructions are not stored. It does not explicitly name sibling tools, so differentiation relies on the preset concept rather than an explicit pointer.

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 a hard precondition: the user must have seen the refined wording and suggested category and explicitly agreed, with a timeout causing no save. The "单次指令不入库" sentence signals a when-not condition. It does not recommend a specific alternative for single-use instructions, so it stops short of full alternative guidance.

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?

With no annotations provided, the description carries the full behavioral burden. It discloses that the call returns the full library and health metrics, that the agent should produce a proposal list, and that changes are applied later via apply_consolidation rather than automatically. It does not explicitly state whether any internal state is modified, but the 'user-driven, then apply' framing strongly implies this call itself does not apply changes.

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 dense and front-loaded, starting with the key call condition. The Chinese portion carries detailed actionable guidance, while the trailing English sentence 'Start a user-initiated consolidation pass' is somewhat redundant with the title and opening clause, but the overall text remains focused and every other sentence 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?

Given the absence of an output schema, the description adequately explains return values (full library and health metrics) and the expected deliverable (a proposal list covering deduplication, contradiction resolution, refinement, retirement, and skill clustering). It also specifies the user-driven decision flow. A more complete description could outline the exact proposal-list format, but the current text gives enough context for correct invocation and downstream behavior.

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 has zero parameters)Skip? Actually the schema has no properties, so there is no parameter semantics burden. The baseline for a 0-parameter tool is 4, and the description appropriately uses the space to explain the intended workflow rather than inventing 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 names a specific verb+resource: starting a memory consolidation pass only on user request. It also differentiates itself from the sibling apply_consolidation by explicitly saying that the proposal list is landed via apply_consolidation, so the agent can tell the two tools apart.

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 opens with an explicit usage condition: '仅在用户主动要求整理记忆时调用' (only call when the user actively requests memory consolidation). It further clarifies that the process is user-driven and that apply_consolidation is the tool that applies the changes, making the division of labor between sibling tools explicit.

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?

With no annotations available, the description carries the burden of behavioral disclosure and does so well: it explains partial-update semantics (unspecified fields stay unchanged), clearing semantics for project/task_type/category, and the trust-reset side effect of substantive content rewrites. The mention of 'user_notice 原样转述' is problematic because user_notice is not a schema property and additionalProperties is false, creating confusion.

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 compact and front-loaded: a quick list of what can be updated followed by three high-value behavioral caveats. The user_notice sentence is tangential and slightly dilutes focus, but otherwise every sentence contributes.

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 no annotations and no output schema, the description covers the key operational context: user approval, field-unchanged behavior, clear semantics, and trust reset. It omits explicit explanations of the two policy enums and does not describe the return/confirmation payload, leaving some gaps but overall enough for an agent to call it correctly.

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 11%, so the description must compensate; it does add useful meaning for partial updates, clearing, and trust reset. However, it does not explain the semantics of risk, confirm_policy, or unattended_policy beyond what the enum values imply, and the user_notice comment references a non-existent parameter.

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 identifies the action ('Update/修改') and resource ('a preset'), and enumerates the categories of changes (content, scope, classification, policy, status). This distinguishes it from the sibling tools that list, recall, delete, or consolidate memories, though the Chinese title '修改记忆' could be slightly ambiguous until the description clarifies 'preset'.

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 an important precondition: the modification must already be user-confirmed ('需用户已确认该修改'), which helps an agent know when it is appropriate to call the tool. However, it does not explicitly name alternatives or exclusion conditions, so an agent must infer when to prefer recall_presets, save_requirement, or delete_memory instead.

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