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 correctly signals a destructive operation via 'Remove', but does not disclose permanence, reversibility, failure behavior, or any side effects. The parameter details add some context, but behavioral transparency remains minimal 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?

The description is compact and well-structured, with a one-line purpose statement and a clear Args block. Every sentence adds value and there is no filler or repetition.

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 two-parameter delete tool, the description covers the essential invocation details: what to remove and how to identify it. An output schema exists, so return-value documentation is not required. The lack of alternative-selection guidance is the only notable gap, though it is more a usage-guideline concern.

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 fully compensates by enumerating valid memory_type values ('semantic', 'episodic', 'lesson') and clarifying that identifier is a key or ID number depending on type. This is exactly the semantic content an agent needs 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 action ('Remove a memory') with a specific verb and resource. It is distinct from Remember (add), Recall (retrieve), and MemoryStats (inspect), but does not explicitly differentiate itself from the sibling PruneMemory, which also removes 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?

No guidance is given on when to use Forget versus PruneMemory or other siblings. The description explains parameters but provides no context for selecting this tool over alternatives, nor any prerequisites or caveats.

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 burden. It discloses a key behavioral trait: learned rules have highest priority and override other memories. However, it does not explain side effects, whether prior conflicting rules are overwritten or merely deprioritized, persistence duration, or how removal works.

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 with the core purpose and priority behavior, followed by an efficient Args section. Every sentence earns its place with no redundant 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?

For a simple two-parameter tool, the description covers purpose, priority behavior, and parameter semantics. It does not explain how it relates to sibling tools like Remember, but the presence of an output schema reduces the need to describe return values.

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. It defines 'rule' with an instructive example and 'category' with its allowed values and default. This gives an agent everything needed to fill both parameters correctly.

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 verb 'Store' and the resource 'correction or rule', and it adds the defining trait that these entries have highest priority and override other memories. This distinguishes Learn from the sibling Remember behaviorally, though it does not name the alternative explicitly.

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 tool is for storing persistent corrections or rules that must always be followed, but it does not explicitly state when to use Learn versus Remember or when not to use it. Usage must be inferred from the stated priority semantics rather than directly guided.

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. 'Show' implies a read-only inspection, and the description names the reported dimensions (counts, age, decay health, tier). It does not disclose potential side effects, whether decay health calculations are triggered, or any access/cost implications, but for a read-style stats tool this is a reasonable baseline.

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, tight sentence that front-loads the action and then specifies the meaningful output categories. No filler, no repetition of the tool name, and every word contributes to understanding.

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 statistics tool, the description is largely complete: it names the resource and the specific metrics reported. The presence of an output schema helps cover return details. Minor room for improvement would be stating whether this is a pure read operation or if it can trigger decay calculations.

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 empty, matching the description. With no parameters to document, the description adds value by clarifying what each result category covers, which is all that is needed.

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 ('Show'), a clear resource ('memory statistics'), and detailed specifics ('counts, age, and decay health per memory tier'). It is clearly distinct from the sibling tools, which perform actions like remembering, recalling, forgetting, or pruning rather than reporting statistics.

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 intended use is implied: call this when you need memory statistics rather than performing a memory operation. However, it does not explicitly state when to use it versus alternatives, nor does it provide any exclusion conditions.

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 states the operation is a hard delete, defines the exact selection rule (max possible decay score below score_floor), and even translates the default into a time estimate (~115 days). It does not mention side effects like return counts, but the output schema covers return structure.

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 first sentence states the purpose, and the following lines explain the selection rule and parameter without redundancy. Every sentence earns its place, and the Args block is clearly formatted for quick scanning.

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 one-parameter tool with an output schema and no annotations, the description covers purpose, destructive behavior, selection criteria, and parameter semantics well. The only notable gap is explicit guidance on when to invoke this tool relative to sibling tools like Forget, but the core operational context is 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 input schema only gives type and default for score_floor, with 0% schema description coverage. The description fully compensates with a dedicated Args section explaining what score_floor controls, the default value, and how to adjust it for a more conservative prune. This exceeds 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 opening phrase 'Hard-delete decayed and soft-deleted episodic memories' names a specific verb (hard-delete), a clear resource (episodic memories), and a precise scope (decayed and soft-deleted). This clearly distinguishes the operation from generic memory operations and from siblings like Recall or MemoryStats.

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 a maintenance/cleanup operation for removing decayed or already-soft-deleted rows, but it never explicitly states when to use PruneMemory versus alternatives such as Forget. There are no exclusions or routing hints to sibling tools, only parameter-tuning guidance ('Set lower to be more conservative').

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 carries the burden of disclosing behavior, and it does so well by explaining how score_threshold applies to semantic cosine and episodic decay scores, and how category only affects lessons. It stops short of explicitly stating side-effect-free behavior, but 'Search' and the parameter semantics strongly imply a read-only operation.

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-organized: a one-sentence summary followed by a tight, informative arg list. Every line adds value, and defaults are included inline, making it easy to scan without unnecessary 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?

Given the technical complexity of the tool and the complete lack of schema-level descriptions or annotations, the description covers all essential aspects: query semantics, filtering modes, threshold behavior, and conditional category logic. Since an output schema exists, omitting return-value details is acceptable and does not detract from completeness.

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 provides no descriptions, so the description fully compensates by explaining every parameter, including valid memory_type values, defaults, threshold semantics, and conditional category filtering. This is exemplary semantic enrichment beyond the raw input 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 opens with 'Search memory by semantic similarity,' a specific verb + resource that clearly defines the tool's function. This distinguishes Recall from sibling write/delate tools like Remember, Forget, and PruneMemory, and from MemoryStats.

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 clearly implies when to use Recall—when retrieving semantically related memory content—but it does not explicitly compare it to alternatives or state when not to use it. The sibling tool names provide context, but the description itself offers no direct routing 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, the description carries the behavioral burden, and it adds useful detail: importance affects decay speed, key is required for semantic memories, and memory_type values map to concrete meanings. It does not discuss overwrite behavior or side effects, but the core persistence behavior is clearly disclosed.

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, front-loaded with the one-line purpose, and uses an Args block where every line adds useful semantic information. No filler or repetition of schema defaults beyond what is helpful.

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 five parameters are all explained, and the output schema relieves the description from explaining return values. The only notable gap is the lack of explicit guidance about when Remember should be chosen over the sibling Learn 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 0%, so the description must fully document parameters, and it does: text, memory_type values, key format ('pref.theme' etc.), importance range/default, and tag format. It adds meaningful context beyond the raw 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 first line, 'Store something in memory,' names a specific verb ('store') and a clear resource ('memory'), and the memory_type options further scope the action. It distinguishes this tool from sibling operations such as Recall, Forget, and PruneMemory, which imply retrieval or deletion.

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 when to use the tool by saying 'Store something in memory' and by explaining memory_type alternatives, but it never explicitly contrasts this with siblings like Learn or states when not to use it. An agent must infer that Recall is for retrieval and Learn may be for lessons.

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