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 a subtle, high-impact behavior: the server uses UTC and will not infer today, so the client must pass the user's local date. It does not overstate side effects, but 'record' clearly signals creation.

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 two sentences with no filler. The essential behavioral warning about UTC is front-loaded in the second sentence and directly actionable.

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 create operation with a full output schema and complete parameter documentation, the description covers the key pitfall an agent would likely miss. It does not mention category validation or sibling alternatives, but the schema states 'Must be a known category' and sibling names are self-explanatory.

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 real value for the date parameter by explaining why the caller must supply the local date and how the server's UTC clock could cause errors. It does not need to repeat the other parameters since the schema already documents them fully.

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 'Record a single expense,' which is a specific verb, resource, and scope. This makes it immediately distinct from siblings like list_expenses, delete_expense, and summarize.

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 wording implies when to use the tool, but it does not explicitly contrast it with alternatives or state when not to use it. The date guidance is operational context, not a when-to-use vs. alternative boundary.

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?

The annotations already mark the tool as destructive and idempotent. The description adds meaningful behavioral context by warning that deleting the wrong row cannot be undone and that user confirmation is required. This goes beyond the annotation flags and helps the agent weigh the consequences of invoking the 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 front-loads the core purpose in the first sentence. The second part provides essential usage and safety guidance without redundancy. It is slightly longer than necessary, but every sentence earns its place by adding important operational context.

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 single-parameter delete operation, the description covers the key aspects: what to delete, how to get the id, and the need for user confirmation. The presence of destructive and idempotent annotations plus a full output schema means the description does not need to explain return values. Minor gaps, such as related data effects, are not significant for this 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?

The only parameter, expense_id, has full schema description coverage: it says 'id of the expense to delete, from list_expenses.' The tool description reinforces this by telling the agent to use list_expenses to find the id, but it does not add fundamentally new semantic meaning beyond what the schema already provides. Baseline 3 is appropriate given 100% 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 opens with the precise action 'Delete one expense, by id', specifying exactly what resource is affected and how it is identified. This clearly distinguishes it from siblings like add_expense and list_expenses, and no other delete tool exists in the sibling set, so there is no ambiguity.

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 instructs the agent to call list_expenses first to obtain the id and to confirm with the user before deleting. It also explains why this is necessary: ids are not guessable and deletion is irreversible. This gives clear when-to-use and safety guidance beyond just naming the operation.

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 disclosure burden. It reveals that categories form a fixed taxonomy and that invalid values are rejected, which is the key behavioral contract for an agent deciding whether to use it. It does not explicitly state that the operation is read-only, though the verb 'List' and the workflow context make that clear.

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 two sentences with no redundancy. The primary action is front-loaded, followed by usage guidance and a validation caveat, making it scannable for an agent.

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 is simple, has no parameters, and has an output schema, so the description need not describe return values. It provides the essential workflow context (when to call and validation behavior) and fits the agent's decision-making needs relative to siblings like add_expense and list_expenses.

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, so the input schema is empty and schema description coverage is 100%. The description does not need to explain any parameter semantics, and the baseline for a zero-parameter tool is 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 opens with the concrete action 'List every valid category and its subcategories', giving a clear verb and resource. It differentiates the tool from sibling expense-management tools by identifying it as a taxonomy lookup rather than a mutation or expense operation.

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 instructs the agent to call this tool 'before logging an expense if you are unsure which category a purchase belongs to', connecting it to the add_expense workflow. It also provides a decisive boundary condition: categories are a fixed taxonomy and anything outside is rejected, so the agent knows when this list is authoritative.

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 ordering ('newest first'), return granularity ('individual expenses' as rows), and default filtering behavior. While it does not explicitly state read-only semantics, 'List' strongly implies a non-mutating operation, and 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?

Two tight sentences with no wasted words. The core purpose and ordering are front-loaded, followed by filter semantics and the alternative-tool pointer.

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 list tool with a rich schema and output schema, the description covers purpose, behavior, defaults, and the main sibling distinction. It does not discuss pagination beyond the `limit` parameter, but that is documented in the schema and not a significant gap here.

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 schema already documents each parameter and its meaning. The description adds useful high-level context about filters being optional and the default result set, but does not need to compensate for any parameter-schema gaps.

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 ('List individual expenses') plus a clear ordering ('newest first'), and explicitly differentiates itself from the sibling `summarize` by contrasting rows vs. totals. An agent can tell exactly what this tool does without opening the schema.

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 says all filters are optional and describes the default behavior when none are set. It also names the alternative tool (`summarize`) and the condition for choosing it ('when you want totals rather than rows'), giving clear when-to-use 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 burden of behavioral disclosure. It adds a non-obvious guarantee: 'Summing happens in Postgres over NUMERIC values, so the total is exact.' This is genuinely useful context about precision. It does not explicitly say the operation is read-only, but the aggregation wording and lack of mutation signals make that reasonably clear.

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 three short sentences, each earning its place: purpose, correctness guarantee, and usage preference. It is front-loaded with the primary function and contains no redundant filler or repeated schema 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 simple read-only aggregate tool with three optional parameters and a present output schema, the description covers purpose, usage guidance, and an important behavioral guarantee. Any remaining edge cases (e.g., empty results, invalid date ranges) are not essential for an agent to invoke the tool correctly, especially since the output schema and parameter descriptions provide the rest.

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 schema already fully documents all three parameters. The description only restates the date-range and category-breakdown concepts, adding no new meaning about date formats, inclusive bounds, or the categorical behavior 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 states a specific verb and resource: 'Total spending over a date range, broken down by category.' This clearly conveys the aggregation behavior and differentiates it from sibling tools like list_expenses. The phrase 'Prefer this over listing every row' reinforces that it summarizes rather than enumerates.

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 instruction 'Prefer this over listing every row and adding them up' explicitly tells the agent to use this tool for totals rather than manually aggregating list results. It does not name the sibling tool list_expenses directly, nor does it state exclusions (e.g., 'use list_expenses when line items are needed'), so it falls just short of fully explicit alternative routing.

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 disclosure burden. It explains that the tool reveals the server-side user, whether expenses are scoped, and that the likely cause of invisibility is an unauthenticated request landing in the shared local bucket. This adds meaningful diagnostic context beyond the tool name.

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 first sentence is a crisp summary, and the second sentence earns its place by explaining practical value. The wording is slightly discursive with the quoted diagnostic scenario, but overall it is compact and 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?

For a simple zero-parameter diagnostic tool with an output schema, the description covers purpose, usage rationale, and a common failure context. It does not explicitly state that the tool is read-only or side-effect-free, but the name and description strongly imply a safe diagnostic operation.

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 input schema is empty, so there are no parameter semantics to clarify. The baseline for a zero-parameter tool is 4, and the description appropriately does not invent parameter guidance.

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: 'Report which user the server sees, and whether expenses are scoped.' This clearly distinguishes whoami from the expense-management siblings by framing it as a diagnostic identity/scoping tool rather than a data operation.

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 a concrete use case: answering 'why can't I see my expenses?' in one call, and advises keeping it permanently rather than as a one-off diagnostic. It does not explicitly contrast against sibling tools or state when not to use it, but the usage context is clear.

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