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

Even without annotations, the description discloses meaningful behavior: it computes per-period averages and deltas, and it explains how to interpret a declining remaining_energy_wh as capacity degradation. It does not discuss auth, rate limits, or side effects, but the analytic and presumably read-only nature of the tool is communicated effectively.

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, starting with purpose, then behavioral interpretation, then parameter details. It is longer than the minimum, but every section adds value, especially given the lack of annotations and schema descriptions.

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 the tool's purpose, interpretation guidance, and all parameter semantics, and an output schema exists to define the return shape. Minor ambiguities like the meaning of 'Nd' in start_date remain, but an agent has enough context to 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?

With schema description coverage at 0%, the description fully compensates by explaining every parameter: bike_id's null behavior, period's valid options, start_date's accepted formats, and end_date's default of today. It even notes that None means all data, which is not available from the schema alone.

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 ('Analyse') and clearly identifies the resource: battery health trends over time, broken out into concrete metrics like charge cycle count and capacity degradation. This also distinguishes it from snapshot tools like bosch_get_capacity or bosch_get_soc, which likely return current values rather than per-period aggregates.

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 clearly frames when this tool is appropriate: for long-term health analysis and comparing early vs recent snapshots to detect capacity degradation. It does not explicitly name sibling tools to avoid or state when not to use it, but the trend-focused wording provides enough contextual 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?

With no annotations, the description carries full transparency burden. It discloses live read/no-cache behavior, lists all included metrics, and explicitly notes that heart rate is not recorded (with guidance to pair a wrist device). This goes beyond a minimal read-only description, though it omits potential rate limits or auth requirements.

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: purpose first, then behavioral context, metric list, a limitation note, and an Args section. The metric list is somewhat long but informative. No filler words; each 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?

An output schema exists, so return structure is covered elsewhere. The description addresses parameters, live data behavior, metric scope, and a key limitation (no HR sensor). It could mention pagination behavior beyond the limit parameter, but overall it is sufficiently complete for a read-only list 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?

Schema description coverage is 0%, but the Args section fully compensates. It explains each parameter's format (e.g., YYYY-MM-DD, YYYY-MM, or '30d'), default behavior, and filtering semantics. This is essential meaning completely absent from the input 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 opens with a specific verb+resource: 'List e-bike rides with per-ride summary metrics.' This clearly distinguishes from sibling tools like bosch_get_activity_detail by focusing on listing multiple rides with summary metrics rather than detailed individual-ride information.

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 listing e-bike rides and gives parameter context, but it never explicitly states when to use this tool versus alternatives. It mentions 'Live read... (no cache)' and HR limitations, but does not name alternative tools or provide when-not-to-use conditions.

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

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

With no annotations, the description carries the transparency burden. It discloses 'Live read' (read-only behavior), downsampling (~500 points per ride), and the return fields. This goes beyond schema/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 and well-structured: opening purpose sentence, return details, dependency instruction, and parameter definition. 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?

For a simple read tool with one parameter and an output schema, the description covers purpose, return values, and parameter provenance. It doesn't address error handling or rate limits, but the tool's simplicity and output schema mitigate that.

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 only defines activity_id as a string with 0% description coverage. The description's Args section explains it as 'The ride UUID from bosch_get_activities', providing critical context for where to obtain the value.

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 'Get the per-point track for one ride' with specific data types (GPS, speed, elevation, cadence, power). It also references bosch_get_activities for the activity_id, distinguishing it from the list 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 usage for retrieving detailed track data for a specific ride, and explicitly instructs to get the activity_id from bosch_get_activities. It doesn't explicitly name alternatives or state when not to use, but the context is clear.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains that each sync captures a snapshot and how latest_only affects results, which adds context. However, it does not explicitly state that this is a read-only operation or disclose any side effects, authorization requirements, or rate limits, leaving some ambiguity 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?

The description is well-structured and appropriately sized. It opens with a clear one-liner, lists return fields, explains the snapshot/history behavior, and then details parameters. Every sentence adds value, and the use of bullet-like formatting improves readability.

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 absence of annotations and the presence of an output schema, the description is largely complete. It explains the tool's purpose, parameters, and the snapshot/time-series model. The only notable gap is the lack of comparison with overlapping sibling tools, which is more relevant to usage guidelines but also affects overall completeness in a broader toolset.

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 has no descriptions for parameters (0% coverage), but the tool description includes an Args section that thoroughly explains each of the 4 parameters: bike_id, start_date, end_date, and latest_only. It provides formats, defaults, and behavioral meaning (e.g., '30d' shorthand, 'return most recent snapshot'), fully compensating for the schema's lack of detail.

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 gets battery state and health for a Bosch eBike, listing the specific data fields returned. This is a specific verb+resource, but it does not explicitly differentiate from sibling tools like bosch_get_soc and bosch_get_capacity, which serve overlapping 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 offers usage guidance for parameters (e.g., 'Use latest_only=False with a date range to see history') but does not explain when to choose this tool over alternatives like bosch_battery_trends or bosch_get_soc. The context implies this is the comprehensive battery snapshot tool, but no explicit exclusions or alternatives are given.

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

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

No annotations are provided, so the description carries the full burden. It discloses that it returns a cached raw API response and that component info may be included, providing some behavioral insight. It does not mention potential side effects, permissions, or rate limits, which would be expected for a 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?

The description is concise and front-loaded, with the primary purpose stated first. The Args section is clearly separated, and every sentence adds value without verbosity.

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 explains return values sufficiently for a single-bike retrieval tool, mentioning full details and cached raw API response. Given that an output schema exists, it does not need to enumerate all fields. The sibling context and lack of complex parameters make the description adequately 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?

The schema only shows a string bike_id, but the description adds crucial context by explaining it is a bike UUID from bosch_get_bikes. This goes beyond the schema and helps the agent select the correct value. However, it could provide more detail on formatting or how to obtain the UUID.

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 a detailed profile for a single Bosch eBike, using a specific verb and resource. It distinguishes itself from sibling tool bosch_get_bikes by explicitly targeting a single bike.

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 context by specifying the bike_id should come from bosch_get_bikes, indicating this tool is for retrieving details after listing. However, it does not explicitly mention when not to use it or name alternative 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?

No annotations are provided, so the description discloses useful behavioral traits: it uses a local cache with auto-sync, and advises running bosch_sync if the list is empty. This goes beyond the empty schema and adds meaningful context about data freshness.

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 and front-loaded with the purpose in the first line. It provides only essential extra guidance (cache and sync) without fluff, and every sentence serves a 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?

For a zero-parameter list tool with an output schema, the description covers the main purpose, return contents, and cache behavior. It also includes a helpful tip about syncing, making it complete for the tool's simplicity.

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 schema coverage is effectively 100%. The description doesn't need to add parameter details, and the baseline for 0 parameters is 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?

The description starts with 'List all Bosch eBikes registered to your Flow account', which is a specific verb (List) and resource (all Bosch eBikes) with a clear scope. This clearly distinguishes it from the singular bosch_get_bike sibling.

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 a concrete prerequisite by saying 'Run bosch_sync first if the list is empty', which tells the user when to perform a sync before calling. It doesn't explicitly exclude alternatives, but the 'all' vs singular naming makes the usage context clear.

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

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

With no annotations, the description carries the full burden. It discloses that data comes from the EU Data Act API and that standard Flow sign-in yields empty results, which is valuable. However, it does not detail potential errors, rate limits, or required authorizations beyond the implied API restriction.

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 main purpose statement, a second paragraph on data source, and a parameter docstring. It is front-loaded and efficient, though the second paragraph could be slightly more concise.

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?

An output schema exists, so the description does not need to detail return values. It covers the data source constraint and empty result scenario. For a read tool with two optional parameters, this is sufficient, but it could mention when to expect non-empty results.

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 has 0% description coverage, so the description must compensate. It clearly explains both optional parameters: part_number and serial_number as filters. This adds meaning beyond the schema's type and default definitions.

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 battery capacity tester diagnostic results, explains what data it includes (remaining capacity percentage), and distinguishes it from sibling tools like bosch_get_batteries and bosch_get_soc. The verb 'get' and resource 'capacity tester diagnostic results' are specific.

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?

There is no explicit guidance on when to use this tool versus alternatives (e.g., bosch_get_soc for state of charge). It mentions data source constraints (EU Data Act API) and empty results with standard sign-in, but does not direct the agent to other tools for common use cases.

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

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the data source depends on sign-in type, explains how component_type matching works (case-insensitive, grouped by stored spelling), and states that invalid values are refused while naming held types. This is unusually transparent.

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 front-loaded with the core purpose, then expands with necessary detail about source, use cases, and parameters. Although longer than minimal, every sentence adds value, and the structured Args section prevents ambiguity.

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 0% schema coverage and absence of annotations, the description is remarkably complete. It covers what is returned, which components are included, how source varies by authentication, filtering behavior, and error handling. An output schema exists, so return value details need not be in 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 schema provides 0% description coverage, but the 'Args:' section fully compensates. bike_id is explained as an optional UUID filter, and component_type gets examples, matching semantics, and failure behavior. Both parameters are meaningfully documented beyond their raw schemas.

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: 'List registered components for your Bosch eBike with software versions.' It then enumerates the component types and data fields, making the tool's scope clear and distinct from sibling tools like bosch_get_batteries or bosch_get_bike.

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 clear usage context: 'Useful for tracking firmware versions and identifying components for warranty or service purposes.' It does not explicitly name sibling alternatives or state when not to use this tool, but the use case guidance is enough to orient an agent.

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

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

With no annotations, the description carries the full transparency burden. It discloses the data source, the condition under which results are empty (standard Flow sign-in), and that the result explains why. This adds meaningful behavioral context beyond a simple 'get' and alerts users to potential empty responses, which is valuable.

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 opening sentence, a contextual caveat, and a concise Args list. It is not overly long and front-loads the purpose. The caveat about EU Data Act and registration is necessary context, so every sentence earns its place, though it could be tightened slightly.

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 (read-only, three optional parameters) and the presence of an output schema, the description covers the essential aspects: what is returned, the data source caveat, and parameter semantics. It lacks explicit mention of return format or error handling, but the output schema likely covers this, so the description is complete enough.

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 has 0% description coverage, but the description's 'Args' section fully compensates by explaining each parameter's purpose, format (including the 'Nd' starting date format), and defaults. This adds significant meaning beyond the schema's bare type/name/default fields.

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 the digital service book history for your Bosch eBike.' It specifies the resource (service records), the verb (get), and differentiates from sibling tools that handle activities, batteries, or bikes. Including 'dates and descriptions of work performed' clarifies the exact data returned.

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 context about when the tool is useful (service history from dealers) and an important caveat (EU Data Act API requirement, empty with standard sign-in). However, it does not explicitly differentiate from alternatives or state when to use this tool over siblings like bosch_get_activities or bosch_get_bikes. The guidance is implied rather than explicit.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool calls the Bosch mobile API live with no caching, which is useful. But it omits error behavior, what happens when the bike is inactive, and doesn't explicitly state that the operation is read-only.

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 appropriately concise, front-loading the purpose and then giving return values, prerequisites, a live-API note, and parameter guidance. Every sentence adds value 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?

For a simple one-parameter read tool with an output schema, the description covers purpose, returned data, prerequisites, data availability, and parameter provenance. It is slightly vague on what 'recently active' means and doesn't cover error scenarios, but overall it is complete enough for an agent to use the tool effectively.

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%, but the description provides essential parameter meaning: 'bike_id: The bike UUID from bosch_get_bikes.' This tells the agent exactly where to obtain the parameter value, fully compensating for the bare input 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 verb and resource: 'Get live state-of-charge from the Bosch ConnectModule.' It also specifies the output values (battery percentage, charging status, remaining energy, range per assist mode), making it distinct from sibling battery-related tools like bosch_get_batteries or bosch_get_capacity.

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 on when the tool is usable: it requires a ConnectModule and only returns data when the bike is powered on, charging, or recently active. However, it does not explicitly mention alternatives or exclusions relative to sibling tools, so it slightly falls short of a 5.

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

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

With no annotations, the description carries the full burden and discloses important behavioral traits: the dependency on the EU Data Act API, the empty result under standard sign-in, and the fact that current firmware is still available via components. It does not explicitly label the operation as read-only, but this is inferable from the verb and 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 front-loaded with a clear one-sentence purpose, followed by a concise explanation of returned data and the essential EU Data Act caveat. The Args section is structured and directly adds parameter details. No sentence is extraneous; the length is appropriate for the tool's complexity.

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 purpose, limitations, and all parameter semantics, and an output schema exists to detail return values. It is slightly incomplete in not mentioning pagination or error behavior, but for a history-retrieval tool with this caveat, it is quite complete. The existing output schema mitigates the need for return format 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?

Schema coverage is 0%, so the description must define the parameters, and it does so thoroughly in the Args block. It explains bike_id as an optional filter, specifies start_date formats (YYYY-MM-DD, YYYY-MM, Nd) and defaults for start/end dates. This fully compensates for the lack of schema 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 software update installation history for a Bosch eBike, using the specific verb 'Get' and a distinct resource. It differentiates from sibling tools by detailing the output includes component update reports with version transitions, and it mentions the EU Data Act API limitation, which further defines its scope.

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 guidance on when to use the tool: it explains that with a standard Bosch eBike Flow sign-in the history is empty, and points users to components for current firmware. This explicitly provides an alternative (bosch_get_components) and a strong when-not condition, exceeding basic usage 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?

With no annotations, the description fully carries the burden of behavioral disclosure. It explains internal storage (SQLite), API source selection based on sign-in type, automatic bike identification, battery time series behavior, and the 'unavailable' status for non-euda accounts, providing deep insight beyond the raw function.

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?

Despite being detailed, every sentence adds value. The structure is clear: a one-sentence summary, followed by behavior, usage context, caveats, args, and return format. It is front-loaded and avoids redundancy, making it easy for an agent 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 description covers all critical aspects for a sync tool: purpose, prerequisites, data types, return format, and account-specific limitations. It even explains edge-case behavior (status 'unavailable' vs empty). Given the simple one-parameter schema and no annotations, this is exceptionally 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?

The input schema offers no description for data_types (0% coverage), but the description compensates comprehensively by listing all valid options, explaining comma-separated multi-selection, providing examples, and stating the default. This fully compensates 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?

The description opens with a clear, specific verb+resource statement: 'Sync Bosch eBike data to the local cache.' It further distinguishes this from sibling getter tools by positioning it as the prerequisite for bosch_get_* tools, making its role unmistakable.

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 run this tool ('before using other bosch_get_* tools, or to refresh after a ride or charge cycle') and provides context about account-type-dependent data availability. This leaves no ambiguity about appropriate invocation timing.

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