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

With no annotations, the description carries the full burden and reveals key behaviors: reuses Chrome session, copies cookies on start and on session expiry, and never asks for a password. It does not specify return format or side effects, but it provides meaningful behavioral context beyond a simple 'check'.

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, well-structured sentence that leads with the main purpose, then uses a dash to add session-reuse context. No redundant or filler words; front-loaded and efficient.

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, no-output-schema check tool, the description is reasonably complete: it explains what it checks, how it authenticates (via Chrome cookies), and that it never asks for a password. The only gap is not explicitly stating the return value (e.g., boolean), but that is a minor omission for such a simple 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?

No parameters exist and the schema is empty, so the description adds no parameter details, but the baseline for zero-param tools is 4. The description does not need to explain any parameters.

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?

Clearly states the specific action ('Check whether the connector is signed in to Uber Eats') and distinguishes itself from sibling data-retrieval tools (orders, details, transactions, export). The verb 'check' and resource 'auth status' are unambiguous.

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?

Implies the tool is a pre-check for authenticated operations (e.g., before fetching orders) but does not explicitly state when to use it versus alternatives or when not to. No exclusions or direct references to 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 carries the transparency burden. It discloses the core side effect (writing a file), the default location, and the return value (path and row count). It omits other behavioral details such as overwrite behavior, auth requirements, and pagination semantics, so it is adequate but not rich.

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 sentences communicate purpose, default behavior, and return value with no filler. The most important 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?

The description is reasonably complete for a 6-parameter tool whose schema covers most inputs, and it tells the agent what to expect back. However, with no annotations and no output schema, it leaves out auth, overwrite, and pagination behavior, which an agent may need to invoke it safely.

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 high at 83%, so the schema already documents most parameters. The description adds modest value by glossing 'items' as 'line items' and noting the default output location, but it does not need to repeat parameter details.

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 ('Write'), a concrete resource ('CSV file'), and the three data categories that mirror the `kind` enum. This clearly distinguishes it from the sibling get_* tools, which imply in-memory retrieval rather than file export.

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 usage context is implied: use when the user wants orders/items/transactions written to a CSV file. However, the description does not explicitly contrast this with get_ubereats_orders or get_ubereats_transactions, nor does it state when not to use it.

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 does this well by specifying what is returned, noting the receipt is included by default, and naming the card-brand/last-4/time details. It does not explicitly state side effects or error behavior, but the operation is clearly a read-only detail fetch.

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 dense sentence that front-loads the purpose and then lists contents in a colon-separated list. Every clause earns its place, though the sentence is slightly overloaded with detail.

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 two simple parameters and no output schema, the description provides a thorough account of what the call returns, including default receipt behavior. Minor gaps remain around what happens when include_receipt is false and any prerequisite auth state, but the definition is largely self-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 100%, so the schema already documents both parameters. The description adds only minor semantic reinforcement by confirming the receipt is included by default, matching the include_receipt default.

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 identifies the resource ('one Uber Eats order') and enumerates the returned categories: items, fare breakdown, receipt charges, tips, refunds, and addresses. It is distinguishable from sibling tools by its singular-order scope, though it doesn't explicitly name an alternative.

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 the tool for fetching details about a specific order, and the schema adds context that the order_id comes from get_ubereats_orders. However, it does not explicitly explain when to choose this over get_ubereats_transactions or get_ubereats_orders, leaving routing largely to inference.

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 must carry behavioral disclosure. It does add useful behavior: source is Uber Eats' order-history feed, pagination stops after start_date, and include_receipts triggers one extra call per order. It does not mention authentication, rate limits, or error behavior, which are notable gaps but not fatal.

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?

Three dense sentences with no wasted words. The purpose is front-loaded, followed by paging behavior and the optional receipt parameter. 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?

With no output schema, the description compensates by listing the returned fields, paging behavior, and the receipt-expansion option. It omits auth concerns and does not explain max_pages/end_date interaction, but those are partially covered by the schema and this is a read-only list 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?

Input schema coverage is 100%, so the baseline is 3. The description adds a small amount of meaning around include_receipts and paging past start_date, but it does not substantially enrich parameter semantics 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 names a specific verb and resource ('List Uber Eats orders'), the ordering ('newest-first'), and the exact returned data (items, fare breakdown). It is clear enough to distinguish from siblings like get_ubereats_order_details and get_ubereats_transactions, though it does not explicitly name 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 gives clear context for when this tool is appropriate: it is the order-history feed and optionally fetches receipts. However, it never states when to use an alternative such as get_ubereats_order_details or get_ubereats_transactions, so the boundary to related tools is left to inference.

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 that tips or refunds posted days after the order are included (behavioral nuance). It also states that charges are kept by their own date. This is useful behavioral context. However, it does not mention pagination behavior, rate limits, or what happens when no transactions exist. It's reasonable 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?

The description is a single, information-dense paragraph with clear structure: it explains what the tool returns, its purpose, and a key behavioral detail. It's not overly long and front-loads the core value. Minor redundancy (e.g., 'one row each' and 'the shape of a card statement') but overall efficient.

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 read-only lookup tool with 100% schema coverage and no output schema, the description sufficiently orients an agent: what data is returned, why to use it, and a caveat about timing. It lacks details on pagination or output format, but since it's a list tool and the schema covers parameters, this is acceptable. Slightly more on response shape could be added, but it's near-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 the schema already documents all parameters. The description adds some context: it explains the significance of lookback_days (late tips/refunds) and store filtering case sensitivity, but most parameter semantics are already covered. Baseline 3 is appropriate since the description adds marginal value 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 that the tool returns every card charge and refund from Uber Eats, one row each, with specific fields (charge time, amount, card brand + last 4, order and store, tip flag). It distinguishes itself from sibling get_ubereats_orders by focusing on financial transactions rather than order details. The purpose is specific and unambiguous.

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 says 'Use this to match bank/card transactions to orders,' which is a clear use case. It doesn't explicitly name alternatives or state when not to use it, but it implies a distinct purpose from get_ubereats_orders (which likely focuses on order content rather than charges). It could be stronger by explicitly contrasting with sibling tools, but 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.