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

No annotations are provided, and the description only states the basic action of recording a file. It does not disclose behaviors like overwrite policy, directory creation, permissions, or rate limits.

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 with a front-loaded main sentence and a separate Args section. It wastes no words, though the Args could be integrated more tightly.

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 no annotations and an output schema, the description provides the core purpose and parameter usage but lacks details on return behavior, file naming, or whether it appends/overwrites. Adequate but with clear gaps.

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 0% schema description coverage, the description compensates by explaining the title as 'one-line headline' and body as 'full markdown explanation' with content guidance. This adds significant meaning beyond the empty 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 records non-trivial decisions as markdown files in a specific directory, with examples of what qualifies. This distinguishes it from sibling tools like add_fact and add_gotcha.

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 explicit guidance on when to use this tool versus alternatives like add_fact or add_gotcha. The description implies usage for decisions but lacks when-not-to-use criteria.

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 must carry the burden. It explains parameters but does not disclose persistence characteristics, side effects, or return values. The existence of an output schema is not acknowledged.

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 informative with a structured Args section, but it is somewhat verbose. Every sentence adds value, but brevity could be improved without losing clarity.

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 logging tool with 6 parameters and no annotations, the description covers purpose and parameter usage well. However, it does not describe the output schema (which exists), leaving a gap in 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?

Schema description coverage is 0%, but the description provides detailed explanations for all 6 parameters (e.g., 'claim: the factual statement (one sentence)', 'file: source file path relative to repo root'). This adds significant meaning beyond the schema titles.

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 ('Record a structured fact'), the resource ('about this codebase'), and the purpose ('so the next agent doesn't have to re-verify it'). It distinguishes from siblings like add_decision and add_gotcha by emphasizing facts that are verified.

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 usage after verification ('fact you just verified') but lacks explicit guidance on when not to use or how it compares to alternatives. No direct contrast with siblings.

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 responsibility for behavioral disclosure. It conveys the append action and file path but omits important details like side effects (e.g., file creation if missing), error behavior, or permissions needed. This is minimal 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?

Two sentences with no wasted words. The first sentence immediately states the action and file location; the second provides usage context. Highly efficient and well-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?

For a simple append tool with one parameter, the description covers the essential aspects: action, target, content format, and usage scenario. An output schema exists, so return values need not be detailed. A minor improvement would be noting if the file is created automatically.

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 sole parameter `note` lacks description in the schema (0% coverage). The description adds value by specifying it is a 'one-line' note and the expected content ('watch out for X'), compensating for the schema 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 specifies the action (append a one-line note), the target resource (`.ai-memory/gotchas.md`), and the type of content ('watch out for X'), clearly distinguishing it from sibling tools like `add_decision` and `add_fact`.

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: 'for surprises that wasted your time and might trip the next agent.' It provides clear context for usage but does not explicitly mention when not to use or list alternatives, which is acceptable given the sibling list.

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 description explains it returns a Markdown document, the effect of fact_limit (cap, most recent first), and implies read-only. Could mention prerequisite 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 short paragraphs: first front-loads purpose and usage, second explains parameter. No wasted words.

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?

Adequate for a simple read tool with one optional param and output schema. Could mention error handling or if memory missing, 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 has 0% description coverage, but description adds 'most recent first' ordering and explains cap, which goes beyond schema properties.

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 returns the entire .ai-memory/ as Markdown, and distinguishes from sibling add_*/list_facts tools by specifying it should be called before tasks to avoid rework.

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 'Call this **before** starting any task' and explains why. While it doesn't list alternatives, the context of sibling tools makes the usage context 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?

No annotations are provided, so the description must convey behavior. It indicates a read operation ('List') with optional filtering, but does not disclose pagination, ordering, or any side effects. Given the simplicity, a score of 3 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?

Two sentences with no extraneous information. The purpose is immediately stated, and the usage hint is brief. Perfect 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?

Despite having an output schema, the description lacks details on filter parameters and return structure. For a tool with four optional parameters, the description is insufficient for an agent to correctly construct calls without additional context.

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 0% schema description coverage, the description should compensate by explaining parameters. It only says 'optionally filtered' but does not mention 'tag', 'source_file', 'since', or 'limit', leaving the agent without guidance on how to use filters.

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 explicitly states 'List recorded facts', clearly identifying the action and resource. It distinguishes from sibling tools like 'add_fact' and 'add_decision' which are write operations.

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 a clear use case: 'Useful when you want only facts relevant to a specific area before reading them.' It implies filtering but does not specify when not to use or alternatives, though the context is adequate.

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