FlightSeatMap

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

The description adds behavioral context beyond the annotations: it notifies by email, requires authentication, and depends on the flight already being in the database. While annotations already show this is a non-read-only, non-idempotent write, the description enriches that with the creation and notification mechanism.

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 tight and well structured: the core purpose comes first, supported by preference examples, then prerequisites. Every sentence adds useful information and there is no filler or redundant restating of the tool name.

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 create-type tool with no output schema and six parameters, the description plus the fully documented schema gives an agent enough to invoke it correctly: purpose, notification behavior, authentication, and a prerequisite. It does not specify the response shape or duplicate-alert behavior, but those are secondary for initial invocation.

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 every parameter including formats, constraints, and conditional requirements. The description only restates high-level preference categories already visible in the enum, adding no extra per-parameter semantics, 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 clear verb and resource: create a seat alert for a specific flight that emails on a matching seat. It distinguishes itself from sibling tools such as delete_seat_alert, list_seat_alerts, and search_flight by describing the alert-creation action and its triggering condition.

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 concrete usage context: authentication is required, the target flight must already exist, and search_flight should be used first when needed. It does not explicitly list exclusions or when-not alternatives, but the prerequisite guidance is direct and actionable.

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 mark destructiveHint true and readOnly false; the description adds two behavioral facts beyond that: the specific resource being destroyed (a seat alert) and the authentication requirement. These are exactly the kind of context the rubric asks for (what gets destroyed, auth needs). No contradiction with 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?

Three short sentences, each with a distinct job: purpose, ID discovery, and prerequisite. No filler or redundant restating of schema details. It is front-loaded with the core operation.

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 delete tool with no output schema and annotations already covering idempotency and destructive behavior, the description is adequately complete. It states what is deleted, how to find the ID, and a prerequisite. A small gap is the absence of return/error behavior, but that is not critical given the simplicity and annotations.

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 already covers alert_id fully with a description. The description reinforces that the ID identifies the alert and, more usefully, tells the agent where to obtain it (list_seat_alerts). This adds practical semantic guidance beyond the bare schema field.

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+resource pair: 'Delete a seat alert by its ID.' It clearly identifies the target resource and the selection criterion, making it distinct from sibling tools like list_seat_alerts (listing) and create_seat_alert (creation).

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 use list_seat_alerts to find the alert ID, which is directly relevant to correctly invoking this tool. It also states the authentication requirement, giving a clear prerequisite. It does not formally discuss when not to use it, but there is no overlapping sibling delete tool, so exclusions are unnecessary.

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 the tool as readOnly and non-destructive, and the description adds that it 'lists complementary remote MCPs' for specific areas. This tells the agent the operation is a side-effect-free lookup and what kind of results to expect, without requiring further behavioral caveats.

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 state the action, the target, and the output categories with no repetition or filler. The main purpose is front-loaded in the first 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 zero-parameter, read-only discovery tool with no output schema, the description is fully sufficient: it defines what the tool does, what it lists, and how it differs from the flight-operation siblings. There are no missing details an agent would need to invoke it 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 tool has zero parameters, so the baseline of 4 applies. The description correctly avoids inventing parameters or adding unnecessary syntax detail, and the schema coverage is already complete.

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 ('Discover') and resource ('other flight & travel MCP servers you can add to your client'), and names the output categories. This clearly differentiates it from sibling tools like search_flight or get_seatmap, which operate on flight data rather than discovering additional MCP servers.

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 makes the use case clear: when a user wants to expand their client with additional flight/travel MCP servers, this is the tool to call. It doesn't explicitly state when-not-to-use or contrast with alternatives, but because it is a zero-parameter discovery tool, the context is sufficient and no siblings perform this function.

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 and destructiveHint=false, and the description's 'Find/Returns' language is fully consistent with that safety profile. It adds behavioral context beyond the annotations by describing the return form — a ranked list plus an interactive seatmap with highlighted recommendations — and the 'available seats' wording aligns with openWorldHint=true (live availability). No contradiction.

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 the action front-loaded and a useful return-format payoff in the second sentence. The preference enumeration is slightly redundant with the schema enum, but it aids quick comprehension without bloating the text.

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 adequately conveys what the caller gets back — a ranked seat list plus an interactive seatmap with highlights. Combined with full schema coverage and safety annotations, an agent has enough to invoke the tool correctly; remaining gaps are minor (how 'best' is ranked, behavior when no seats match).

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 four parameters (flight_number, preferences, cabbin_class, flight_date). The description echoes the preferences enum values but adds no format, constraint, or relationship details beyond what the schema provides; baseline 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 opens with a specific verb and resource — 'Find the best available seats on a flight' — and specifies the matching behavior against preferences, followed by a concrete output description ('ranked list and an interactive seatmap with recommended seats highlighted'). It clearly states what the tool does, but it does not explicitly distinguish it from close siblings like interactive_seat_finder or get_seatmap.

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 embeds a clear usage context: this is the tool to use when seats must be ranked against user preferences such as window, aisle, extra legroom, exit row, or quiet zone. It stops short of naming alternatives or exclusions, so an agent must infer when to prefer it over get_seatmap or get_seat_info.

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 and destructive=false; the description adds a non-obvious behavioral side effect—that the tool renders an interactive seatmap—which is useful and not contradicted by annotations. It could further explain dynamic pricing/availability, but the annotations already signal safety.

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 sentences. The first front-loads the core purpose and output categories; the second adds a key behavioral detail. No redundant or filler text.

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?

No output schema exists, so the description must carry return-value information; it lists content categories but leaves format ambiguous (e.g., JSON data vs rendered seatmap) and does not clarify the relationship to get_seatmap. It is viable but incomplete for an agent.

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 all parameters already have meaningful descriptions in the schema. The tool description adds no parameter-specific semantics beyond grouping flight_number and seat_number into the notion of a 'specific seat on a flight.'

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 states a specific action ('Get detailed information') on a specific resource ('a specific seat on a flight') and enumerates the data categories (characteristics, cabin class, availability, pricing). It also adds a second function—'renders the interactive seatmap'—which slightly blurs the boundary with sibling get_seatmap, so differentiation is not complete.

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 guidance is provided on when to use this tool versus siblings such as get_seatmap, find_best_seats, or interactive_seat_finder. The statement about rendering a seatmap implies possible overlap but no alternatives or exclusions are named.

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 read-only, non-destructive, and open-world behavior. The description adds valuable behavioral context: the data is cached (potential staleness), output is both text and interactive visual, and the source is a database. This goes beyond the structured annotations without contradicting them.

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 core function and output are front-loaded, followed by the cache caveat and alternative. Every sentence earns its place.

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 tool with 2 parameters and no output schema, the description is complete: it explains the output format, the data scope, the caching behavior, and how to get fresh data. An agent has all the context needed to call it 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 has 100% description coverage with explicit formats and examples for both flight_number and flight_date. The description adds no additional parameter-level meaning, 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 specific verb and resource ('Get the seat map for a flight from our database') and details exactly what is shown (all seats, cabin classes, characteristics, availability as text and visual seatmap). This scope distinguishes it from siblings like get_seat_info and the fresh-data search_flight.

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 names search_flight as the alternative for fresh/updated data and states the condition ('Returns cached data'), making it clear when to select this tool vs its sibling. This provides an explicit when/when-not routing decision.

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 readOnly and non-destructive, and the description adds valuable astra info: 'Free — no auth required' and the returned fields. Because there is no output schema, enumerating ratings, comments, seat number, cabin, and helpfulness votes helps set expectations. No contradiction with annotations is present.

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 tight sentences, front-loaded with the core action and resource, then the optional filter, then output fields and auth status. Every sentence add value with no fluff.

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 list tool with fully documented parameters and a no-auth annotation, the description covers the essence: what it returns, optional filtering, and access requirements. The lack of an output schema is compensated by listing the key output fields. Nothing critical for invoking it correctly appears missing.

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 already fully describes both parameters (100% coverage), so baseline is 3. The description adds meaningful semantics by framing seat_number as an optional filter and confirming the resource is tied to a flight, which reinforces the schema rather than just repeating it.

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 ('user-submitted reviews for a flight'), with an optional seat filter. It also enumerates the data shown (ratings, comments, seat number, cabin, helpfulness votes), clearly distinguishing it from sibling tool's like seat maps or seat infor. The scope is unambiguous and no tautology is present.

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 gives clear context: use this tool to retrieve user-submitted reviews for a flight, optionally filtered by seat number. It does not explicitly name alternatives or state when-not-to-use, but the purpose is specific enough that an agent can infer when this tool matches the need.

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 readOnnly and non-destructive behavior. The description adds value by disclosing that the tool renders an interactive seatmap rather than just returning data, which is useful behavioral context beyond the schema and annotations. No contradiction with 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, no fluff. The core function is front-loaded and the usage hint follows immediately. Every sentence earns its place.

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 two-param read-only tool, the definition is complete: it says what it does, what it outputs at a high level (interactive seatmap), and when to choose it. No output schema exists, but 'renders an interactive seatmap' gives enough return-shape context for an agent.

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 flight_number and flight_date. The description does not add any parameter-level detail beyond what the schema provides, 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?

Clearly states what the tool does: recommends best seats on a flight and renders an interactive seatmap. This distinguishes it from siblings like get_seatmap or find_best_seats via the interactive rendering and the explicit 'default when no preferences' positioning.

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?

Tells the agent when to use it: as the good default when the user wants seat help but hasn't specified preferences. It doesn't name a specific alternative or state when-not to use it, but the 'default when no preferences' phrasing implies a clear selection rule.

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 convey read-only, non-destructive intent. The description goes beyond this by requiring authentication and by stating the tool returns active and inactive alerts including seat, priority, days until flight, and last check time. Useful behavioral context for an agent.

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 sentences. The primary action and scope are front-loaded, the returned fields are summarized, and the authentication note is valuable. 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?

For a zero-parameter, read-only list operation without an output schema, the description adequately describes the resource, scope, and returned information. No missing information is needed to invoke it 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 has zero parameters, so there are no parameter semantics to describe. Baseline of 4 is appropriate; the description adds no irrelevant param 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?

States a clear verb-resource pair: 'list' + 'your seat alerts'. Clarifies scope (active and inactive) and lists the fields returned, so an agent can distinguish this from create_seat_alert and delete_seat_alert without inspecting schemas.

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. The behavior is implied by the name, but there is no mention of cases where create_seat_alert/delete_seat_alert or search tools would be more appropriate.

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 it as non-read-only and open-world, and the description adds meaningful behavioral details beyond that: authentication via OAuth, consumption of a search credit, paid-plan access to new flights, and freshness of the data. These are valuable operational traits not encoded in the structured fields. No contradiction exists between the description and 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?

The description is concise, with three sentences that each add information: the core purpose, the auth/account requirement, and the cost/access/result details. The main purpose is front-loaded. It is slightly dense, but every sentence earns its place without 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 two-parameter tool with full schema coverage and no output schema, the description covers the essential operational context: return type (interactive seatmap), authentication, credit usage, and data freshness. It does not mention error cases or what happens when a flight isn't found, but this is minor given the tool's simplicity and the other covered details.

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 provides full documentation for both parameters: flight_number includes an example, and flight_date includes format and default. The description itself adds no parameter-specific detail beyond the general action of searching for a flight. With 100% schema coverage, a 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 states a specific action ('Search for a flight') and a concrete result ('get fresh seatmap data', 'Returns an interactive seatmap with the results'). It clearly distinguishes the tool as a search operation that yields seatmap data. However, it does not explicitly differentiate itself from sibling tools like get_seatmap or interactive_seat_finder, so it falls short of perfect clarity.

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 useful usage context: requires a signed-in account, uses OAuth, consumes a search credit, and paid plan users can fetch new flights. However, it offers no explicit guidance on when to choose this tool over alternatives, such as get_seatmap for already-cached seatmaps, nor any when-not-to-use conditions. The usage case is implied rather than stated directly.

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