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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It usefully reveals that macmon NAC info is automatically included if configured, and hints at the return format. However, it omits crucial behavioral traits such as whether the tool is read-only, potential error states (e.g., device not found), or authentication requirements, leaving gaps in transparency.

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 Args and Returns sections, and the information is relevant and concise. It includes necessary details without extraneous text. The only slight inefficiency is the repetition of the hostname example, but overall it is appropriately sized and 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?

Given the absence of an output schema, the description thoroughly explains what the tool returns (hardware, OS, last user, group, agent status, macmon NAC status). It covers the primary use case and the automatic macmon inclusion. However, it does not address error scenarios or permissions, leaving minor gaps for a tool with a single parameter and no output schema.

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 only a type and title for device_id with no description (0% coverage). The description compensates well by explaining it accepts either a GUID (from list_devices) or a hostname, which adds significant meaning beyond the schema. A higher score would require more detail on format constraints or validation.

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 all details of a single device, specifying it accepts both GUID and hostname. It distinguishes from siblings like list_devices (which returns multiple devices) and get_device_update_status (which focuses on update status), 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?

The description provides clear context on when to use this tool (to get full details of one device) and mentions it accepts both GUID and hostname. However, it does not explicitly exclude or compare to alternatives like search_devices, which could also return a single device. Thus, guidance is strong but not exhaustive.

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 indicates a read operation and specifies the return data structure. However, it does not disclose behavior such as error responses for invalid device_ids, performance impact, or permission requirements. It is adequate but not thorough.

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 four succinct sentences that immediately convey the tool's purpose, followed by structured Args and Returns sections. No extraneous 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 tool's simplicity and lack of annotations, the description provides sufficient context: input parameter explanation and output fields. It covers the necessary information for an AI agent to use the tool correctly, though it omits error handling or edge 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?

The description adds significant meaning to the 'device_id' parameter by providing example formats (hostname or GUID), which is not available in the input schema (0% coverage). This compensates well for the schema's lack of description.

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 returns the Windows update status of a single device, listing specific missing updates and timestamps. This distinguishes it from sibling tools like 'list_devices' and 'report_update_compliance' which operate on multiple devices or aggregate 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 implies use for a single device via the 'device_id' parameter, but does not explicitly guide when to use versus alternatives (e.g., when to use 'list_devices' for multiple devices). No when-not-to-use or prerequisites are provided.

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 provided, so description carries full burden. It discloses return value (complete object with type, folder, category, description) and input parameter, but lacks mention of error behavior or permissions. Still adds significant value.

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?

Efficiently structured with Args and Returns sections in three lines. No fluff, purpose 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?

Complete for a single-get tool: describes input and output. Could mention error handling for invalid id, but overall sufficient given no output schema.

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?

Adds substantial meaning beyond schema: explains definition_id is a GUID and references search_job_definitions as the source. Schema coverage is 0%, so this description fully compensates.

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 uses specific verb 'returns all details' and resource 'job definition', clearly distinguishing from siblings like list_job_definitions and search_job_definitions by referencing the id field from the latter.

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 states that the definition_id comes from search_job_definitions, providing direct usage context. Does not list exclusions, 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?

No annotations are provided, and the description does not disclose behavioral traits beyond the fact that it returns details. It does not mention whether it is read-only, any authentication requirements, error behavior (e.g., if instance_id is invalid), or rate limits. Since annotations are absent, the description should cover these aspects more thoroughly.

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 with three short sections (purpose, Args, Returns) that are front-loaded and free of unnecessary words. Every sentence adds value, and the formatting is clean and easy to parse.

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, no nested objects, no output schema), the description adequately explains the return value including steps, timestamps, and status. However, it lacks error behavior details and does not mention any prerequisites or permissions, which would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaningful context by specifying that instance_id is 'GUID der Job-Instanz (id-Feld aus list_job_instances)', clarifying its origin and required format. However, it does not provide further constraints like expected length or pattern, which would elevate the score to 5.

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 returns all details of a single job execution, using the specific verb 'Gibt zurück' and the resource 'Job-Ausführung'. It differentiates from sibling tools like list_job_instances (which lists multiple) and start_job (which creates jobs) by focusing on a single instance detail retrieval.

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 flow by noting the instance_id comes from list_job_instances, but it does not explicitly state when to prefer this tool over alternatives (e.g., for full details vs. summary) or when not to use it. More explicit guidance on prerequisites or context would improve the score.

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 full burden. It discloses that the return object contains 'total' and 'software' (alphabetically sorted) with fields name, version, publisher, installDate. This provides sufficient behavioral context 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 well-structured with Args and Returns sections. Every sentence adds value, and the main purpose is front-loaded. No unnecessary verbiage.

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 lack of output schema, the description adequately explains the return format including sorting and fields. It covers both parameters and output structure. Minor gaps like error handling or pagination are acceptable for this simple 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?

With schema description coverage at 0%, the description fully compensates by explaining device_id accepts hostname or GUID, and query is an optional search term with default behavior ('Leer = alle installierten Programme'). This adds significant meaning 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 the verb 'Gibt zurück' (returns) and the resource 'installierte Software auf einem Gerät'. It distinguishes from sibling tools by focusing specifically on software inventory, unlike get_device or list_devices which deal with device info or listings.

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 specifies acceptable inputs for device_id (hostname or GUID) and explains the query parameter as optional search term. While it doesn't explicitly contrast with alternatives, the narrow purpose makes usage self-evident.

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 provided, but description discloses that it accepts hostname or MAC, auto-resolves MAC via baramundi for hostnames, and returns a detailed object. No side effects or destructive actions are implied but none expected.

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 summary, Args, and Returns sections. No redundant sentences, though 'Args:' and 'Returns:' formatting could be streamlined for an AI agent. Still clear and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter and no output schema, the description covers input format, behavior, and all return fields comprehensively. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description adds significant meaning: explains identifier can be hostname or MAC with examples, and details return fields. Compensates well for lack of schema documentation.

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 it queries VLAN and NAC status of a client in macmon. It distinguishes from sibling tools which focus on devices, jobs, and reports.

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 clear context for when to use (querying VLAN/NAC status) but does not explicitly state when not to use or mention alternatives. Implied usage is adequate.

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 burden. It mentions returning a single page and the return format, but does not disclose read-only nature, authentication, or other behavioral traits beyond basic pagination.

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, front-loaded with a clear purpose and warning, then structured with Args and Returns sections. Every sentence adds value without waste.

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 annotations, no output schema, and 0% schema coverage, the description provides sufficient context: it explains the tool's purpose, parameters, return structure, and contrasts with a sibling. Missing an explicit statement of read-only behavior, but overall adequate.

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 adds meaning for all three parameters: type with examples, limit with default and max, page with starting value. Return fields are listed, compensating 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 clearly states the tool returns a single page of the device list for paginated overviews, using a specific verb and resource, and distinguishes from the sibling search_devices 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?

Explicitly directs when to use (paginated overviews) and when not to (searches), and provides the exact alternative search_devices with arguments.

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 pagination behavior (20 per page, page number parameter) and return structure (object with 'data', 'totalItems', 'totalPages'). However, it does not explicitly state that the tool is read-only or safe, though that is implied by its listing nature.

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: two sentences plus structured Args/Returns. The first sentence immediately states the purpose, and every part adds value. No redundant or vague language.

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 (1 parameter, no output schema), the description provides complete context: it explains pagination, the parameter, and the return format. The tool's purpose is fully covered for an agent 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 schema has only one parameter 'page' with a default of 0. The description adds meaning: 'Seitennummer ab 0 (Standard: 0)' (Page number starting from 0, default: 0). Since schema description coverage is 0%, the description fully compensates by clarifying the parameter's semantics.

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 'Listet alle Job-Definitionen seitenweise auf (20 pro Seite)' (Lists all job definitions page by page). It uses a specific verb ('list') and resource ('job definitions'), and distinguishes from the sibling tool 'search_job_definitions' by noting that for targeted search, the search tool should be used.

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 provides usage guidance: 'Für gezielte Suche besser search_job_definitions verwenden' (For targeted search, better use search_job_definitions). This tells the agent when not to use this tool and suggests an alternative, making the decision 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?

No annotations exist, so the description must carry the burden. It discloses pagination and return format but omits authentication needs, rate limits, or read-only nature, though it reasonably implies no side effects.

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 with clear Args/Returns sections. It is slightly longer due to field listing, but 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?

Given the simple tool with one parameter and no output schema, the description covers pagination, return structure, and fields adequately, though it could clarify ordering or date range.

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 sole parameter 'page' is explained with its default and starting index, adding meaning beyond the schema definitions despite zero 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 lists recent job executions for all devices, using a specific verb and resource. It distinguishes from siblings like 'get_job_instance' (single instance) and 'list_job_definitions'.

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 alternatives. The description lacks context for selecting this over sibling tools like 'get_job_instance'.

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 of behavioral disclosure. It explains that the tool returns an object with 'total', 'byState', and 'problematic' fields, indicating it is a read-only reporting operation. It does not mention any destructive side effects, which is appropriate.

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, with separate sections for general purpose, arguments, and return values. Every sentence adds value, and there is no redundant or extraneous 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 complexity (1 parameter, no output schema), the description is complete. It explains the single parameter, the return structure, and the overall purpose. The sibling tools are diverse but this tool's niche is clearly defined.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explicitly explains the 'type' parameter, listing possible values ('windows', 'mac', 'linux') and noting the default is 'windows'. This adds significant meaning beyond the input schema, which only provides a title and default 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 it shows the health status of the baramundi client agent across all devices and identifies devices with stopped or missing agent. It uses specific verbs ('zeigt', 'identifiziert') and specifies the resource, distinguishing it from sibling tools like report_inactive_devices or report_failed_jobs.

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 implicitly indicates usage by listing the type parameter (windows, mac, linux) which defines scope. It does not explicitly provide when to use vs. alternatives, but the purpose is clear enough that an agent can infer appropriate 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?

No annotations exist, so the description must fully disclose behavioral traits. It only mentions the parameter and return format, but omits side effects, permissions, data freshness, pagination, or error handling—critical for a report tool.

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 extremely concise—three short lines covering purpose, parameter, and return structure. No unnecessary words; front-loaded with the main action.

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 tool with no output schema, the description covers key aspects but lacks detail on the content of 'instances' and does not differentiate from similar reports. Additional context on use vs. list_job_instances would improve completeness.

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 description adds meaning for the 'limit' parameter (default and purpose), which compensates. It could be improved with constraints (e.g., maximum value), but is sufficient for usage.

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 explicitly states it lists failed job instances sorted by error count, using a specific verb (listet...auf) and resource. This clearly distinguishes it from sibling tools like list_job_instances that likely list all instances.

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 error reporting but provides no explicit guidance on when to use this tool versus alternatives (e.g., list_job_instances) or conditions that warrant its 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?

No annotations provided, so description carries full burden. It transparently describes the return object structure (total, devices sorted by daysSinceLastSeen descending). No side effects described but likely a read-only report.

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 docstring format with Args and Returns. Every line provides necessary information 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?

Covers purpose, parameters, and return format. Lacks examples or edge cases, but for a simple report tool it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but description adds meaning for all three parameters: days threshold, device type options, and output limit. Defaults are stated again but with context.

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 tool finds devices not seen for a number of days (specific verb and resource). It distinguishes from sibling tools like list_devices or search_devices by focusing on inactivity.

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 description implies use for finding inactive devices but does not mention when not to use or recommend other 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?

With no annotations, the description carries the burden. It discloses the output structure (object with 'total' and 'distribution' sorted descending) and the parameter details. No side effects or auth needs mentioned, but as a read-only report, this is adequate.

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 relatively concise and front-loaded, but mixing German and English (German purpose, English param/return docstring) may reduce clarity. Still efficient overall.

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 parameter with no output schema, the description sufficiently covers input, output, and purpose. No missing elements for a read-only aggregated report.

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 single parameter 'type' has 0% schema description coverage, but the description fully compensates by listing allowed values ('windows', 'mac', 'linux') and the default ('windows'), adding significant meaning beyond the schema's type and default.

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 creates an overview of OS versions for all managed devices and explicitly mentions its use for identifying outdated versions. This distinguishes it from sibling report tools like report_agent_health or report_update_compliance.

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 use case ('helpful to identify outdated OS versions') but does not explicitly state when not to use or compare with alternatives. No exclusions or conditions 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 implies a read-only report by nature, but does not explicitly declare it as non-destructive or mention any permissions/rate limits.

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 purpose sentence, a use case, an Args block with explanations, and a Returns block. It is concise and front-loaded, though slightly longer than minimal.

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 output schema, the description includes a Returns section describing the output structure (summary and devices), which adds completeness. Behavioral details like read-only are not explicit, but overall it covers purpose, parameters, and output.

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% (no descriptions in JSON schema), but the description fully explains all three parameters: min_missing (minimum missing updates), category (critical/security/other/any), and limit (max devices). This adds complete meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it shows a compliance overview of all Windows devices by update status, with a use case of identifying outdated/unpatched devices. This distinguishes it from siblings like get_device_update_status which likely targets individual devices.

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 includes 'Ideal um veraltete oder nicht gepatchte Geräte zu identifizieren' as a use case, but does not explicitly state when not to use or compare to alternative tools like get_device_update_status.

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 must disclose behavior. It explains case-insensitivity, partial matching, and searched fields (hostname, IP, registeredUser, logicalGroup). It also describes the return structure. This is transparent for a search tool, though it omits any potential scope or pagination details beyond the limit parameter.

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 concise summary followed by an Args section. It is front-loaded and each sentence provides value. Slightly verbose but still efficient.

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 annotations and no output schema, the description covers essential aspects: input parameters, search behavior, and return structure. It is complete enough for typical use, though it could mention default ordering or scope (all devices).

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%, so the description carries full burden. It thoroughly explains each parameter: query (searched fields with examples), type (device types with default), and limit (default value). This adds significant meaning 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 it searches devices by hostname, IP, user, or group, using a specific verb and resource. It distinguishes from siblings like get_device (single device) and list_devices (full list).

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 the search behavior but does not provide explicit guidance on when to use this tool versus alternatives like get_device or list_devices. The usage context is implied through the sibling tool list, but no exclusions or recommendations 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?

While the description details functionality and return format, it fails to disclose behavioral traits such as read-only nature, authentication requirements, or side effects. With no annotations provided, the description should compensate but does not.

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, well-structured with a clear purpose, parameter list, and return value description. Every sentence provides valuable 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 the simple parameter set and absence of output schema, the description adequately covers input parameters and return format. It could mention pagination or ordering, but overall it is sufficiently complete for a 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 description adds significant meaning to all three parameters, explaining query as a search term with examples, type as prefix or short name with a predefined list, and limit as max results. This greatly compensates for the 0% schema description 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 searches job definitions by name or type (prefix), providing a specific verb-resource pairing. It distinguishes from siblings like list_job_definitions (which likely lists all) by specifying search capabilities.

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 (search by name/type), including behavior for empty query or type. However, it does not explicitly state when not to use it or provide alternatives like list_job_definitions for full listings.

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 discloses behavior: default dry_run=True means no actual execution, dry_run=False triggers real execution. Also explains start_if_already_assigned parameter. 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 sections (WICHTIG, Workflow, Args, Returns), but slightly lengthy. Front-loaded with key warning about dry_run. 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?

Despite no output schema, description explains return values for both dry_run modes. Provides workflow context with sibling tools. Does not mention error handling or permissions, but overall comprehensive for a 4-parameter 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 description extensively explains all four parameters: job_definition_id and endpoint_id as GUIDs, dry_run with default and effect, and start_if_already_assigned with behavior. Adds critical meaning beyond 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 starts a job on an endpoint (Softwareverteilung, Scan, Skript etc.) and provides a specific workflow involving sibling tools, distinguishing it from 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?

Explicitly outlines when to use: after obtaining job_definition_id and endpoint_id via search_job_definitions and get_device/search_devices. Emphasizes starting with dry_run=True for preview and only then dry_run=False for execution.

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