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

With no annotations, the description fully discloses behavior: it tracks turn count, returns consolidate true at configured intervals, and provides statistics. It clarifies that the tool prompts consolidation rather than automatically executing it, avoiding ambiguity. No contradictions.

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 appropriately sized: a short declarative sentence followed by a bullet list of return fields. Every sentence contributes meaning, and the key information 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?

Given the tool's simplicity (no parameters, no output schema), the description is complete. It explains when to use it, what it returns, and how to interpret the result. No gaps for an agent to understand its role in the dialogue loop.

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, making schema coverage 100% by default. The description adds significant value by explaining all return fields (turn_count, consolidate, turns_since_consolidation, next_consolidation_at) and their semantics, which is well beyond the baseline of 4.

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's purpose: tracking conversation turns and automatically triggering memory consolidation. It uses specific verbs ('追踪轮次并自动触发记忆固化') and distinguishes it from siblings that deal with memory operations (e.g., store_memory, decay_memories).

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 '每轮对话结束时调用此工具' (call at the end of each conversation round). It also explains that when consolidate=true is returned, the agent should perform a memory consolidation via store_memory. However, it does not explicitly mention when not to use it or provide alternatives, so it loses a point.

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 the decay mechanism and that low-quality memories are not deleted but can be awakened. However, it does not mention side effects, permissions, irreversibility, or whether it affects persistent storage, leaving some 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 well-structured and efficient: it starts with the main purpose, explains the mechanism, gives usage advice, and then details the parameters in a bulleted format. Every sentence adds value with no superfluous 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?

For a maintenance tool with two optional parameters and no output schema, the description covers purpose, behavior, usage guidelines, and parameters. It does not mention return values (likely none), but this is acceptable given the tool's side-effect nature. Minor gap: no mention of whether the tool returns a status or confirmation.

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 0%, so the description must compensate. It explains decay_rate as a coefficient multiplying memory quality (default 0.95) and batch_size as the upper limit per round (default 100). This adds clear meaning beyond the schema's type and default fields, though it could provide more detail on valid ranges.

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 executes a round of memory quality decay (forgetting mechanism). It explains that low-quality memories sink but are not deleted, and can be awakened later. This distinct purpose differentiates it from siblings like store, recall, or link.

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 explicitly advises periodic invocation (e.g., every 24 hours or every 100 dialogues) rather than each dialogue. It gives clear usage context but does not explicitly compare to sibling tools or state when not to use.

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?

Without annotations, the description implies read-only behavior via '查看' (view), but lacks explicit statements on side effects, auth, or rate limits. It adds some context but not full transparency.

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?

Description is concise, front-loads purpose, then use cases, then parameter info. No unnecessary text.

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 stats retrieval tool with no output schema, the description lists what is included (total memories, type distribution, etc.), which is fairly complete. Could elaborate on return format but not critical.

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 0%, but the description explains the sole parameter 'detail_level' with default and allowed values (summary/detailed), adding meaning 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 clearly states the tool retrieves overall memory stats, listing specific components (total memories, type distribution, etc.). It gives a specific verb 'view' and resource 'memory space', and though it doesn't explicitly differentiate from all siblings, the purpose is distinct.

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 mentions two use cases: at conversation start and when user asks about memory amount. While it doesn't state when not to use, the contexts are clear and sufficient.

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 provided, so description carries full burden. It discloses that manually created links have higher weight than automatic links, and explains parameter effects. Does not mention return values or error cases.

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 brief paragraphs plus a parameter list with no redundant information. Each sentence adds value, and structure is clear.

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?

Context is good: explains purpose, usage, and parameters. Lacks details on return type or failure behavior, but given it's a mutation tool with no output schema, the description is mostly complete. Could mention prerequisites like memory existence.

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 0%, but description compensates fully by explaining all parameters: source_id, target_id, link_type with options (semantic, emotion, causal), and strength_override with default automatic. Adds meaning beyond 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 clearly states the tool's purpose: manually creating gravity links between two memories to supplement automatic computation. It specifies the action (link), the resources (memories), and contrasts with automatic linking.

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 explicitly states when to use: '当你在对话中识别到两条记忆存在明确的深层关联时调用' (when you identify a clear deep connection). It implies when not to use by noting it supplements automatic algorithm omissions, but does not list alternatives.

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 provided, so description carries full burden. It describes the tool as a query operation with no mention of destructive effects, but does not disclose rate limits, required permissions, or behavior when no parameters are given. Adequate 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?

Very concise: one sentence for purpose, one for usage guidelines, then a bulleted list of parameters. Every sentence adds value, front-loaded with purpose.

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?

Covers purpose and parameters well. No output schema, so return values are not described. Could mention what the tool returns (e.g., list of memories). Otherwise complete for a read tool with 6 optional params.

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 description fully compensates by explaining each parameter: memory_type, event_id, time_range (with JSON format example), keyword, limit (with default and upper bound), sort_by (with options). Adds meaning beyond 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 clearly states the tool's purpose: precise querying of memories by time/type/event coordinates. It distinguishes from the sibling tool 'tool_recall_by_gravity' by explicitly stating it is for precise search, not associative 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?

Explicitly says to use when user specifies time range or type, and that it is not suitable for associative retrieval. This provides clear when-to-use and when-not-to-use guidance, implying alternatives.

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 bears full burden. It explains the behavioral traits: diffusion activation along gravitational links, depth controlling recall and noise, and threshold for link activation. It does not cover all possible side effects or permissions, but for a recall tool, the description is transparent enough.

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 well-structured: starts with purpose and usage, then parameter details. It is front-loaded with the most important info. However, the parameter section is somewhat verbose with Chinese formatting and emojis, but every sentence adds value.

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 7 parameters and no output schema, the description covers parameters well but does not describe the return format or error behavior. It mentions activating associated memories but no structure. For full completeness, it should hint at the output (e.g., list of memory objects).

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. It does so excellently: explains query as natural language, query_tags as required extraction by AI, max_activations default and max, gravity_threshold adjustment guidance, memory_type_filter, time_range format, depth default and max. This adds critical meaning 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 clearly states the tool's purpose: '引力联想检索' (gravitational associative recall). It explains the mechanism of spreading from entry nodes along gravitational links to activate associated memories. It distinguishes itself from sibling tools like tool_recall_by_coordinate by positioning itself as the core differentiation for topic-based or associative recall.

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 explicitly says when to use this tool: when the user mentions a topic, needs associative recall, or says 'do you remember...'. It also provides guidance on adjusting gravity_threshold for broader or more precise recall. It does not explicitly state when not to use, but the sibling tools imply alternatives.

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 provided; description only mentions automatic polar coordinate assignment. Lacks details on side effects, constraints (e.g., uniqueness), idempotency, or return values, which is insufficient for a mutation 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?

Description is efficient, front-loaded with purpose and usage, includes examples and parameter list. Well-structured and readable.

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?

No output schema; description does not explain return value or confirmation of registration. Missing details on duplicate names or error conditions, making it incomplete for real use.

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 0%, but description briefly explains each parameter: event_name required, parent_event_id for sub-events, description optional. Adds modest value beyond schema types.

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?

Description clearly states it registers a new event into an event space with automatic polar coordinate assignment. Examples differentiate it from sibling tools like store_memory or recall.

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 specifies when to call (new independent event clues) and provides examples. Encourages proactive identification by the agent, but does not mention explicit alternatives or when not to use.

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 provided, but the description comprehensively explains parameter behaviors, including defaults and recommendations (e.g., semantic_tags critical for retrieval). It lacks details on return value or side effects, but for a write tool 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 detailed and well-structured with a parameter list, but slightly verbose. Every sentence adds value, though minor trimming could improve 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?

Given the 9 parameters (2 required) and no output schema, the description covers all inputs thoroughly and provides usage context. It could mention what the tool returns (e.g., success/failure), but overall sufficient.

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%, but the description compensates fully by explaining all 9 parameters, including valid enum values, defaults, and usage recommendations. This adds significant meaning beyond 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?

The description clearly states the tool's purpose: '存储一条新记忆到球状记忆空间' (store a new memory in spherical memory space). It explicitly distinguishes itself from sibling tools (e.g., recall, decay) by being the only tool for storing new memories.

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 specific usage guidance: call when there is noteworthy information, batch at natural conversation pauses rather than sentence by sentence. This clearly tells the agent when and how to use the tool.

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