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

No annotations are provided, so the description carries the full behavioral burden. It clearly signals a write/logging side effect to the memory log, which is useful, but it does not disclose whether the log is append-only, whether it finalizes the reasoning ticket, or whether it can be reverted. The existence of revert_reasoning_transaction suggests transaction semantics that the description leaves unstated.

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 with no redundant words or filler. Every part contributes to identifying the action, the subject, and the destination, making it concise without being vague.

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 schema fully documents all required parameters, including the success/failure enum, so the tool is invocable from structured data alone. However, with no output schema and no annotations, the description leaves return behavior and transaction lifecycle implied, and it does not mention how this tool relates to revert_reasoning_transaction. It is adequate but not fully 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?

Schema description coverage is 100%, so all five parameters are already documented with descriptions and the outcome parameter has an enum. The description only conceptually maps to the parameters ('outcome of a reasoning cycle') and adds no parameter-level meaning beyond what the schema provides. 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 uses a specific verb ('Logs') and identifies both the resource ('outcome of a reasoning cycle') and the destination ('memory log'), so the tool's function is immediately clear. This distinguishes it from sibling tools such as search_learnings (read-only) and revert_reasoning_transaction (undo), even without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used after a reasoning cycle to persist an outcome and lesson, but it never states when to use it explicitly or contrasts it with alternatives like search_learnings or revert_reasoning_transaction. An agent can infer timing from the name and schema, but the guidance is only implicit.

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 explicitly states the destructive effect: 'removing the ticket and any associated learning logs.' It does not mention irreversibility or permissions, but the core destructive 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?

Two sentences with no wasted words. The first sentence states the action and effect, and the second gives the use case. Every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains what the tool does, what it removes, and when to use it, which is sufficient for a simple two-parameter destructive operation. It does not describe return values or failure modes, but no output schema is present and those are not essential for invoking this tool 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?

The input schema covers both parameters fully at 100% coverage with clear descriptions. The tool description adds no parameter-specific detail beyond referring to the reasoning ticket, so the baseline score of 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 names a specific verb ('Reverts') and resource ('a reasoning transaction'), and clarifies the concrete effect: removing the ticket and any associated learning logs. This clearly distinguishes it from sibling tools like search_learnings, set_reasoning_budget, and log_reasoning_reflection.

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?

'Use this to recover from a corrupted or unwanted state' provides explicit context for when the tool should be invoked. It does not name alternatives or exclusions, but the sibling tools are sufficiently different in purpose that no confusion is likely.

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 full behavioral burden. The verb 'Searches' clearly indicates a read-only operation, and 'within the current project' scopes the behavior. However, it does not disclose matching semantics, result ordering, or whether the search is restricted beyond the current project.

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 active sentence that front-loads the main verb and includes only useful scope information. There is no filler, redundancy, or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only search with a fully described parameter schema, the basic invocation is covered. However, there is no output schema and the description does not state what the returned matches look like or how results are ordered/limited, so an agent must infer some 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?

Schema description coverage is 100%, so the baseline is 3. The description reinforces the meaning of workspace_path by mentioning the 'current project', but it does not add meaningful detail about query syntax or the limit parameter beyond what the schema already 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 uses a specific verb ('Searches'), names the resource ('learning bank'), and adds scope ('past reflections within the current project'). It is immediately clear what this tool does and how it differs from siblings like log_reasoning_reflection or revert_reasoning_transaction.

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: use this when you need to retrieve past reflections from the learning bank. However, the description does not explicitly state when to prefer this over alternatives or when not to use it, leaving the contrast with siblings implicit rather than stated.

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

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

There are no annotations, so the description carries the behavioral disclosure burden. It only says the tool returns a string instruction; it does not disclose whether the workspace_path is read, whether the operation is side-effect-free, or how the token budget affects behavior. The name set_reasoning_budget hints at mutation without confirming it.

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 with no filler, efficiently stating a transformation and its output. It loses some value because it omits a required input, but the structure itself is clear and compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description needs to explain the return value and invocation context more fully. It only says 'string instruction,' omits workspace_path, contradicts the required token_budget, and gives no sense of what the agent should do with the returned instruction.

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 documents all three parameters, so the baseline is 3. However, the description says token_budget is optional while the schema marks it required, and it entirely omits workspace_path, which actively misleads the agent about how to invoke the tool.

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 concrete transformation: take a task description and token budget, return a string instruction for adaptive reasoning. It is not a tautology and is distinguishable from the sibling tools, though it omits workspace_path and the tool name suggests a setter while the description describes a generator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: when an AI needs a string instruction to reason adaptively. However, it provides no explicit guidance about when to prefer this tool over search_learnings, revert_reasoning_transaction, or log_reasoning_reflection, and no exclusion criteria.

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