Servd Agentic Ordering

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

The description adds meaningful behavior beyond the readOnly/idempotent annotations: it discloses intentionally omitted fields (contact, address, notes, line items) and describes the state-driven polling contract. This gives the agent realistic expectations without contradicting annotations.

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 filler. The primary purpose and key polling instruction are front-loaded, and the omission disclosure is compact and useful.

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 output schema exists, the description does not need to detail return fields. It covers purpose, source of order_id, polling lifecycle, and response omissions, making it complete for an agent to invoke 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?

Schema coverage is 100% and the schema already describes order_id as the machine-facing orderId from place_order. The description reinforces that origin and adds the sandbox simulation use case, but it does not materially expand parameter meaning 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?

States a specific verb ('check') and resource ('payment and fulfillment state for an order created by place_order'), plus a secondary use case for signed sandbox simulations. This clearly distinguishes it from creation (place_order), lookup (get_venue), and validation (validate_cart) tools.

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 instructs when to poll ('while state is awaiting_payment') and when to stop by enumerating terminal states. It does not name alternatives or exclusions, but the sibling tools are clearly distinct and the polling guidance is concrete.

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?

Annotations already cover the read-only, idempotent, and non-destructive nature. The description adds useful behavioral context beyond that: it returns a summarized projection (slug, name, city) and that the slug is essential for other tools. It does not discuss pagination or limit behavior, but that is minor for this simple read 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?

Two sentences with no filler: the first front-loads the main purpose and return values, the second explains the key parameter. Every phrase adds value, including the note about slugs being needed downstream.

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 listing tool with an output schema and safety annotations, the description is mostly complete. It could improve by mentioning the limit/pagination behavior or explicitly directing agents to get_venue for single-venue details, but these are not critical 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?

The description reinforces the query parameter ('Pass query to filter by name or city'), but the schema already documents this. The limit parameter has no semantic description in either the schema or the tool description, leaving its purpose implicit despite having constraints.

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 finds restaurants/venues on Servd and returns a list of slugs, names, and cities. This distinguishes it from singular tools like get_venue, especially by emphasizing the slug as the key needed by every other tool.

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 to use when you need to discover venues and obtain slugs for downstream calls, saying 'the slug is what every other tool needs.' It gives clear context but does not explicitly state when not to use it or how it differs from get_venue.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: prices are major-unit decimal strings in the menu currency and required modifier groups declare min/max selections.

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 concise, front-loaded sentences. The main action is stated immediately, followed by the most important downstream usage and data-format details. No filler or redundancy.

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 single-parameter read-only tool with an output schema, the description is complete. It explains what the menu contains, how prices are represented, the modifier-group semantics, and how the returned ids connect to cart/order tools.

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%, with the slug parameter already documented as 'The venue slug.' The description does not add new meaning to the parameter beyond what the schema provides, so the baseline of 3 applies.

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 clear verb and resource: 'Get a venue’s full live menu.' It is obviously distinct from venue discovery and order/cart tools, though it does not explicitly name an alternative such as get_venue for venue metadata.

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 useful usage context: the returned item ids are intended for use in validate_cart and place_order. It does not explicitly state when not to use this tool or name alternatives, but the downstream guidance implies the correct workflow.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context about the returned content (current open state, availability, policies, payment mode) but does not disclose behaviors such as not-found handling or data freshness. This is acceptable given the strong annotation coverage.

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 sentence that front-loads the action and subject, then lists data fields with no filler words. Every element earns its place, and it is easy to parse quickly.

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 has one required param, a documented schema, an output schema, and safe-read annotations. The description clearly states what data will be returned, so an agent has everything needed to invoke it correctly. No missing prerequisites or edge-case instructions are necessary for this simple read 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 only parameter, slug, is fully documented in the schema ('The venue slug.') with 100% schema description coverage. The description confirms the singular nature of the lookup but adds no new semantic detail beyond the schema, so the 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 ('Get') and resource ('one venue') and enumerates the exact data returned: opening hours, current open state, pickup/delivery availability, policies, and payment mode. This clearly distinguishes it from siblings like find_venues (search) and get_menu (menu items), so an agent can confidently select this tool for venue-level details.

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: it is for retrieving details for exactly one venue, identified by slug. It does not explicitly state when not to use it or mention alternatives like find_venues, but the singular scope and field list make the primary use case unambiguous.

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?

Annotations already mark this as non-read-only, idempotent, and potentially destructive, and the description adds meaningful context: server-side price recalculation, no-charge sandbox behavior, 30-minute expiration for unpaid online orders, and the need to surface orderNumber and customerLinks. There is no contradiction with the annotations.

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?

Every sentence carries essential operational information, and the most important instruction is front-loaded: only place the order after showing latest validation and receiving confirmation. Though dense, it is not wasteful and earns its length given the tool's side effects.

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 high-stakes order-placement tool with 11 parameters, nested objects, idempotency requirements, and real-world consequences, the description covers preconditions, sandbox behavior, token validity, price recalculation, result display, polling, and expiration. An agent has enough context to invoke it correctly and 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 coverage is moderate at 64%, so the description must add value for the critical parameters. It explains why confirmation_token and idempotency_key matter, clarifies that prices are server-recalculated, and describes sandbox implications. Some parameters like fulfillment and payment_method are left to the schema, but the key workflow-critical semantics are covered.

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, unambiguous action — 'Create a REAL restaurant order' — and clearly differentiates this from validation/checking by naming validate_cart and check_order. The term 'REAL' signals that this is the side-effecting placement step, distinct from cart validation.

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 explicit preconditions: show the latest validate_cart result, obtain explicit diner confirmation, and pass the confirmation token. It also describes the sandbox simulation alternative and explicitly instructs polling check_order afterward, giving the agent a complete workflow.

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?

Annotations already carry readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: no order is created, and the pricing is authoritative (server recalculated). However, it doesn't disclose behavior on invalid carts or failures, though the output schema may cover that.

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, front-loaded with the core purpose, and no wasted words. The timing instruction and data minimization guidance are packed efficiently into the second sentence.

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 tool with rich input and output schemas plus strong annotations, the description covers the essential purpose, non-mutating behavior, and call timing. It could add a brief note about validation failure behavior, but given the output schema exists, the description is substantially 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 only 25%, so the description carries more responsibility for parameter meaning. It does add some guidance by saying to send only minimum contact and fulfillment data, but it does not explain key parameters like items, combos, customer, delivery, or payment_method. This leaves a significant semantic 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 states a specific verb ('validate'), a specific resource ('proposed cart'), and the exact scope: calculating lines, discounts, fees, tax, and total. It also explicitly distinguishes itself from order creation ('without creating an order'), which separates it clearly from place_order.

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 clear, explicit invocation context: 'Call this immediately before asking the diner to confirm.' This tells the agent when to use it. It doesn't explicitly name alternatives or exclusions, but the 'without creating an order' phrasing implies the key distinction from place_order.

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