Airline Starlink Tracker

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

Annotations already signal readOnly/openWorld/idempotent, but the description adds substantial behavioral detail: the three return categories (FIRM YES / FIRM NO / probability estimate), the ~2-day assignment publication timeline, and the fall-through behavior to predict_flight_starlink. This goes beyond what annotations alone convey.

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 sentences, with the usage trigger front-loaded and no filler. Every sentence adds distinct value: when to use, what it returns, and when to use the alternative.

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 no output schema, the description covers the key return semantics, the timing uncertainty, and the routing to predict_flight_starlink. For a read-only lookup tool with 100% schema coverage and rich annotations, nothing essential is missing for correct 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 all four parameters thoroughly. The description adds little beyond the schema: it reinforces the flight-number/date trigger but does not meaningfully expand param meaning. 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 the exact trigger ('does my flight have Starlink/WiFi?') and the specific resource (tracked-airline flight number and date). It also distinguishes itself from predict_flight_starlink by clarifying that check_flight returns firm yes/no when assignment exists and falls through to an estimate otherwise.

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 when to use ('with a specific tracked-airline flight number and date'), gives an exclusion ('For dates further out'), and names the alternative (predict_flight_starlink) plus the reason (extra latency). This is strong routing 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds the nature of the returned data (counts, percentages, per-aircraft-type breakdown), which is useful context beyond the annotations. No contradictions found.

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 usage trigger is front-loaded, followed by a compact summary of the output and a clear redirection to an 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 read-only stats tool with no parameters and no output schema, the description fully covers when to use it and what it returns. The annotations handle safety, and the description handles selection criteria. Nothing an agent needs to decide is 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 tool has zero parameters, so the schema provides complete coverage. Per the rubric, a baseline of 4 is appropriate since there is no ambiguity to resolve and the description doesn't need to add parameter-level 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 uses the specific verb 'returns' and clearly identifies the resource as 'Starlink installation counts and percentages' across fleets. It distinguishes this tool from check_flight explicitly by stating 'Not for per-flight checks', making the purpose 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?

It gives an explicit trigger ('when the user asks...') and an explicit exclusion ('Not for per-flight checks — use check_flight for that'), naming the alternative. This provides clear guidance on when to select this tool over 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?

Annotations already declare readOnlyHint and idempotentHint, so the bar is lower. The description adds useful behavioral detail: returns the 50 most recent by default, allows a limit up to 500, and specifies the exact fields returned (tail numbers, aircraft types, operators, install dates). This goes beyond the 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 zero filler. The use-case is front-loaded, the exclusion is explicit, and the return details are compact. 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?

Given the tool's simplicity, the rich annotations, and the absence of an output schema, the description covers everything needed to call it correctly: what it returns, the default/max limit, and the clear alternative for flight queries. Nothing essential is 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?

Schema description coverage is 100%, so both parameters are fully documented in the schema. The description only reiterates the default and max limit, adding no new semantic nuance beyond the schema. 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 (list) and resource (aircraft with Starlink), and explicitly differentiates itself from search_starlink_flights. It gives concrete example user queries and lists the returned fields, making the tool's purpose 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?

It clearly states when to use the tool ('when the user asks about the planes themselves') and provides an explicit exclusion and alternative ('Not for finding flights — use search_starlink_flights for that'). This leaves no ambiguity about 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?

Annotations already declare readOnly, idempotent, and non-destructive, so the description only needs to add behavioral context. It does add useful scope (comparison across every tracked airline) and an external fallback, but the parenthetical 'nonstop' conflicts with the schema's max_stops parameter, and 'Starlink odds' is left undefined.

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 with the trigger first, then the behavior, then the special-case routing. There is no filler, and the most decision-relevant 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?

All parameters are documented and annotations cover the safety profile, but there is no output schema and the description never states what the returned itinerary or odds look like, nor how 'full-coverage' versus 'partial baselines' are presented. The ambiguous 'nonstop' wording also leaves a gap about how connecting itineraries are handled.

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%, and every parameter already has a clear description with examples and bounds, so the description need not repeat parameter details. The 'X to Y' phrasing maps only trivially to origin and destination, providing no additional semantic 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 pairs a concrete trigger ('what's the best way to fly X to Y with Starlink?') with a specific behavior ('Compares Starlink odds... across every tracked airline'), so it goes beyond merely restating the title. It stops short of 5 because it never names a sibling tool such as predict_route_starlink or search_starlink_flights, and the 'nonstop' parenthetical is ambiguous.

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 an explicit when-to-use trigger and a special-case routing rule for multi-stop UA itineraries (connect to unitedstarlinktracker.com/mcp). However, it does not enumerate the sibling tools as alternatives, leaving the agent to infer when this tool should be preferred over the other Starlink prediction tools.

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 read-only, idempotent, and non-destructive, and the description is consistent with those. It adds valuable behavioral detail beyond annotations: reliability tiers by observation count, coverage differences between flight-number ranges, and the low-result branch where the date triggers a route lookup and returns a ready-made plan_starlink_itinerary call.

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 dense but every sentence earns its place: trigger condition, output definition, reliability caveat, fleet-coverage nuance, and schema-relevant guidance. Key decision-relevant content is front-loaded in the first sentence, and the rest directly improves correct invocation.

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 two-parameter, read-only prediction tool with no output schema, this is complete. The agent knows what triggers the call, what the result means, which confidence tiers matter, how fleet split should be sourced, and what happens downstream on low-probability results. Nothing essential is 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 documents both parameters with 100% coverage, so the baseline is 3. The description meaningfully adds semantics, especially for date: it must be passed when known, affects behavior on low-confidence results, and enables a pre-filled downstream call. It also clarifies flight_number formats and marketing-number preference.

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 trigger ('will my flight have Starlink?'), a temporal condition (too far out or no date), and a concrete output (probability from historical observations). It also implies differentiation from route-level prediction by focusing on flight number and mentions a sibling (get_fleet_stats) without confusion.

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 clearly says when to use the tool: when the user asks about Starlink on a flight with a date too far out or no date. It also gives an explicit alternative call for fleet coverage data (get_fleet_stats). However, it does not explicitly say when not to use it relative to siblings like check_flight or predict_route_starlink, leaving some routing implicit.

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 handled. The description adds useful behavioral detail: ranked results, no connection logic, and the empty-result semantics. It does not contradict the annotations, though it does not discuss anything like pagination or limits beyond the schema.

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-loaded: use cases first, then mechanics, then the alternative tool. It loses full marks because the 'both origin and destination are required' statement sits uneasily against the 'touching an airport' behavior and the origin-only example, slightly muddying an otherwise efficient structure.

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 still explains what is returned (flight numbers ranked by probability) and what an empty result means. The main gap is the unresolved destination-optionality question around 'touching an airport' and 'what Starlink flights serve airport X?', which an agent would need clarified to use the tool reliably in all described cases.

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%, so the baseline is 3; the description adds the important context that origin and destination are both required for route comparison. However, this conflicts with the schema's required-parameter count of 0 and the origin-only example, which creates genuine ambiguity for an agent deciding what to pass.

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 identifies the resource and behavior: a single-route lookup returning tracked airlines' flight numbers ranked by Starlink probability. It also explicitly names the key sibling alternative, plan_starlink_itinerary, while the route-vs-itinerary distinction is concrete enough to prevent confusion.

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?

Gives explicit trigger examples ('which flights between X and Y have Starlink?'), an explicit exclusion ('no connection logic or coverage-ratio ranking'), and an explicit alternative tool recommendation for connection planning. It also tells the agent what an empty result means, which is practical guidance for interpreting the response.

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, destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: the 'confirmed vs predicted' data distinction, the ~2-day window, and the limitation that 'aircraft assignments aren't published further out'. 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 sentences, each earning its place: the usage trigger, the core function and data nature, and the parameter constraint plus alternative routing. The most decision-relevant 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?

For a simple 3-param search with 0 required params and no output schema, the description covers when to use, what it returns, the time window, and param requirements. It doesn't describe the return format, but for a flight-list search this is a minor gap given the clarity of 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 baseline is 3. The description adds the relational constraint 'Pass at least one of origin or destination (both narrows to a single route)', which is not present in the individual parameter descriptions. This meaningful cross-parameter guidance justifies a 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?

States a specific verb and resource: 'Returns CONFIRMED Starlink flights in the next ~2 days'. The 'firm schedule, not prediction' phrase distinguishes it from prediction siblings, and the description names predict_route_starlink/plan_starlink_itinerary as alternatives. An agent can immediately tell this apart from the other 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?

Gives an explicit trigger condition ('Use when the user asks "what Starlink flights leave from X tomorrow?"') and explicit exclusions with named alternatives ('for later dates use predict_route_starlink or plan_starlink_itinerary instead'). Nothing 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.