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

Annotations already declare readOnlyHint true and destructiveHint false. The description adds value by detailing what the check includes (availability, key validity, reachability). It does not contradict annotations and provides useful behavioral context.

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 two sentences, front-loading the action and purpose. Every sentence adds meaningful information without 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?

Given no parameters and an output schema exists, the description is fairly complete. It explains what is checked and shown. Could slightly expand on scope ('all configured APIs'), but sufficient for a simple status 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?

There are no parameters, so schema coverage is 100% trivially. Per guidelines, baseline is 4. The description appropriately does not need to detail 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?

The description clearly states the tool checks the connection status of all configured transport APIs, specifying what it shows: availability, API key validity, and service reachability. This distinguishes it from sibling tools which query specific transport data.

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 does not explicitly state when to use this tool or when to avoid it, nor does it mention alternatives. Usage is implied from context (checking health before using other transport tools), but lacks explicit 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 declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds valuable caveat that discounts may not be fully considered and that route info is included. No contradictions.

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?

Well-structured with Args, examples, and a note. Each sentence adds value, though slightly verbose. Front-loaded with main purpose.

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?

Covers purpose, parameters, usage notes, and caveats. Output schema exists, so return values need not be described. Missing explicit when-to-use compared to siblings, but overall 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?

Input schema has no property descriptions (0% coverage). Description fully explains each parameter with format, examples, and defaults, compensating completely for the schema 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?

Description clearly states purpose: query ticket prices for Swiss public transport connections, including fare calculation and route info. Specific verb 'abfragen' and resource 'Ticketpreise' distinguish it from sibling tools like transport_trip_plan.

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?

Provides examples and a note about consulting sbb.ch for binding prices. Implies usage for price queries via examples, but does not explicitly contrast with sibling tools or state when not to use.

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 description adds context about the returned information (carriage order, equipment, track assignment) beyond the annotations which already indicate read-only, idempotent, and non-destructive behavior. 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?

The description is well-structured with clear sections (purpose, args, examples, typical questions) and front-loaded with the main purpose. While somewhat lengthy, every sentence adds value; minor redundancy could be trimmed.

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 complexity and the presence of an output schema, the description covers all necessary aspects: purpose, parameter details, usage examples, and typical queries. It is comprehensive for an AI agent to select and invoke 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?

Despite 0% schema description coverage, the description thoroughly explains each parameter: train_number with examples, railway_company with allowed values, operation_date with default and usage note, show_details with three options. This fully compensates for the schema's lack of descriptions.

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 retrieves train composition and carriage order for Swiss trains, listing specific details like classes, sectors, equipment, and track assignment. It distinguishes from sibling tool `get_train_occupancy` by focusing on composition rather than occupancy.

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 explains when to use the tool with parameter defaults and examples, and lists typical questions it can answer. However, it does not explicitly state when not to use it or mention alternative tools, though the context of sibling tools makes this 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 indicate readOnlyHint=true (safe read), and the description adds valuable behavioral details: automatic removal of train type prefixes, default date range up to 3 months ahead, and operator code mappings. No contradictions.

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 well-structured with a brief intro, bulleted args, and examples. It is comprehensive without being verbose; every sentence adds value.

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 complexity (two query modes, multiple parameters) and the existence of an output schema, the description covers all necessary context: parameter semantics, usage patterns, and examples. It is 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 coverage is 0%, so the description fully compensates by explaining each parameter with examples, default values, and allowed values (operator codes). It clarifies the two modes and which parameters are needed for each.

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 it retrieves occupancy forecasts for Swiss trains, specifies the output format (per class and segment), and distinguishes itself from sibling tools like transport_departures or get_train_composition by focusing on occupancy.

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 explains two usage modes (by train number or by route) and gives examples, guiding the agent on when to use each. However, it does not explicitly contrast with alternatives or state when not to use the tool.

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, so the agent knows this is a safe read operation. The description adds context about the types of data returned (Zugausfälle, Verspätungen, etc.), which supplements the annotations without 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?

The description is well-structured with a clear headline, bullet-like list of disruption types, parameter documentation, and examples. It is a bit lengthy but every part adds value, especially the examples. A slightly more concise phrasing might improve it.

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 has 3 optional parameters and no required ones, the description covers the main functionality and parameters. With an output schema existing, return values need not be explained. However, it could mention that the data is specific to Swiss public transport, which is already clear from the description.

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 3 parameters with 0% schema description coverage, so the description carries full burden. It provides explicit meanings for each parameter, including filter text examples, language codes, and max results range, adding significant value beyond the bare 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 retrieves current disruption reports in Swiss public transport, listing specific types of disruptions (train cancellations, delays, track changes, line closures). This distinguishes it from siblings like transport_departures or transport_trip_plan, which serve different purposes.

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 examples and explains parameters (filter_text, language, max_results) with default values. It implies use cases (e.g., 'Alle aktuellen Störungen' with no arguments, filtering by city or line). However, it does not explicitly state when not to use or compare to sibling 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 indicate readOnlyHint=true and destructiveHint=false, so the description's job is lighter. The description adds value by noting real-time information including delays, and analogizing to a digital departure board, which helps set expectations 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 concise: three sentences plus a bullet list. It front-loads the primary function and uses efficient language with no 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?

Given the tool's moderate complexity (real-time data, multiple parameters), the description covers the main purpose, prerequisite, and return fields. It does not explain all edge cases, but an output schema likely handles that. The description is sufficient for an agent to select and invoke the 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?

Despite schema description coverage reported as 0%, the input schema itself contains detailed descriptions for each property (e.g., stop_id, time, limit). The tool description adds little new parameter-specific meaning beyond what is already in the schema, though it does reiterate the prerequisite for stop_id.

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's purpose: 'Get upcoming departures or arrivals at a Swiss public transport stop.' It uses a specific verb+resource pattern and distinguishes itself from sibling tools by explicitly referencing transport_search_stop as a prerequisite.

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 instructs users to 'Use transport_search_stop first to get the stop_id,' providing clear usage guidance. It also implies the tool is for real-time departure boards. However, it does not explicitly state when not to use it or compare it to alternatives like transport_nearby_stops.

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. The description adds value by detailing the return format (metadata, resources with URLs and formats) and confirming no destructive side effects, aligning 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?

The description is concise (three sentences) and front-loaded with the main purpose. Every sentence adds value: purpose, return details, and usage hint. No redundant 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?

Given the low complexity (single parameter, read-only operation) and the presence of annotations and output schema, the description covers all necessary aspects: what it does, what it returns, and how to use it with a sibling 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?

Although context signals schema coverage as 0%, the input schema actually provides a descriptive example for dataset_id. The description reinforces the parameter's role and references the sibling tool for finding valid IDs, adding semantics 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 the tool's purpose: 'Get full details of a specific transport dataset.' It specifies the resource type (dataset) and distinguishes from sibling tool transport_search_datasets by explaining the first step to find the dataset ID.

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 to use transport_search_datasets first to find the dataset ID. Provides clear context but does not include when-not-to-use or alternatives beyond this.

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?

Description adds that it returns id, name, coordinates, distance info, and Swiss coordinate bounds. Annotations already cover safety (readOnly, idempotent, non-destructive), so 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?

Very concise: four short sentences, each adding distinct value. No redundant or filler 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?

With output schema present, description mentions return fields. Lacks mention of authentication or prerequisites, but for a simple read-only tool this is acceptable.

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 already provides detailed descriptions for all parameters. Description only repeats the coordinate bounds already in schema, adding no new semantic value beyond a baseline for high schema 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 clearly states the tool finds public transport stops near a geographic location, distinguishes from siblings like transport_search_stop by specifying coordinates, and mentions Swiss coordinate bounds.

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?

Provides usage context (near schools, addresses, POIs) and geographic constraints. Lacks explicit when-not-to-use or alternative tool mention, but implied by coordinate focus.

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=false, making behavior clear. The description adds concrete return structure (name, description, formats, download URLs) and catalog size (~90 datasets), enriching understanding beyond 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 very concise: three sentences front-loading the purpose (search catalog), listing content categories, and specifying return fields. No unnecessary words, every sentence adds value.

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 annotations (readOnly, idempotent) and existence of an output schema, the description provides adequate context: catalog scope, example dataset types, and return structure. It could mention pagination or result count, but it's sufficient for a straightforward search 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 schema already describes both parameters (query required, limit optional with constraints). The description adds value by providing concrete search term examples ('gtfs', 'fahrplan', 'realtime') and clarifying that results are datasets from opentransportdata.swiss, which aids parameter interpretation.

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 searches the Swiss transport open data catalog (~90 datasets), enumerating example dataset types (timetables, GTFS, etc.) and return fields. It distinguishes from sibling tools like transport_search_stop or transport_departures by focusing on dataset 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 implies usage for finding transport datasets and gives search examples ('gtfs', 'fahrplan'), providing clear context. However, it does not explicitly state when to use this vs. alternatives like transport_search_stop or transport_get_dataset, though sibling differentiation is achievable from context.

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 provide readOnly, idempotent, and non-destructive hints. Description adds that the tool searches across all Swiss public transport stops and returns specific fields (id, name, coordinates, transport modes), clarifying the scope and output.

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, front-loaded with action, each sentence adds value: purpose, scope, and output. 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?

Given the presence of an output schema and low parameter complexity, the description covers what the tool does, when to use it, and what it returns (including the critical role of stop IDs). Complete for an agent to use 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 already includes descriptions for both parameters (query and limit), so the description does not need to add parameter details. It adds no extra 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?

Description clearly states the verb 'Search' and the resource 'Swiss public transport stops and stations by name'. It distinguishes from sibling transport_nearby_stops which searches by location. Also explains the output's role for 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?

Description indicates when to use (before transport_departures and transport_trip_plan) and implies use by name rather than location, contrasting with sibling transport_nearby_stops. No explicit exclusions but clear context.

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, so the description's claim of returning trips without modification aligns. It adds useful context such as operating like the SBB app and specifying return structure, but does not disclose performance characteristics or edge cases beyond the annotation.

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 well-structured paragraphs that front-load the core purpose, followed by usage tips and return overview. No unnecessary sentences 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?

The description covers primary use, input advice, and return structure, but lacks details on optional parameters (time, limit) and their defaults. Given an existing output schema, the description is mostly complete, but minor omissions prevent a perfect score.

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 0% according to context, but the description mentions origin and destination parameters and their preferred inputs. However, it omits the time and limit parameters, which are described in the schema. The description adds value by suggesting stop IDs, but incomplete coverage reduces the score.

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 defines the tool as planning a journey between two locations in Switzerland, explicitly stating it returns multiple trip options with transfers, durations, and transport modes, distinguishing it from sibling tools like transport_search_stop.

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 explicit guidance on when to use stop IDs from transport_search_stop for best results and notes that place names work but may be slower, effectively differentiating between this tool and its sibling for stop search.

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