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 the return fields, update frequency (multiple times daily), data sources, and that no API key is required. It also mentions potential errors (CVEClientError). Rate limits are not mentioned, but the information provided is substantial.

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: main purpose, what it provides, use cases, return details, notes, and raises. Every sentence adds value, and it is appropriately sized 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 tool has no parameters and an output schema exists (as indicated by context signals), the description fully covers the return fields and provides additional useful context (update frequency, sources, no API key needed). It is complete for an agent to understand and use the 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 input schema has zero parameters, so the baseline is 4. The description does not need to add parameter information since there are none. It correctly provides context about what the tool returns instead.

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 information about the CVE database status and last update time. It lists exactly what it provides (last update timestamp, total CVE count, etc.) and distinguishes it from sibling tools like cve_search_by_id which search for specific CVEs.

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 a 'Common Use Cases' section that implicitly guides when to use the tool (verify data freshness, check database health, etc.), but it does not explicitly say when not to use it or mention alternatives. 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, so the description carries full burden. It details data source (cve-search.org), auth requirements (no API key), update frequency, return structure, not-found response, and error handling. All behavioral traits are disclosed.

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, but it is somewhat verbose (e.g., bullet lists of return fields). It front-loads the main purpose and uses headings, which aids 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 single parameter and rich output, the description covers input format, output structure, error cases, source attribution, use cases, and examples. Despite an output schema existing, the description adds valuable context.

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 complete parameter semantics: format (CVE-YYYY-NNNNN), examples, and explicit documentation in the 'Args' section. This fully compensates 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 starts with 'Get detailed information about a specific CVE by its identifier', which is a specific verb+resource combination. It clearly distinguishes from sibling tools like cve_search_by_vendor and cve_database_status through the focus on identifier-based lookup.

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?

Common use cases are listed (security research, incident response, etc.), but the description does not explicitly state when to avoid this tool in favor of alternatives like cve_search_by_vendor. The context is clear enough for typical usage.

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 fully discloses behavioral traits: sorting by severity/recency, error handling (found=false response), API requirements (no key), case-insensitivity, and raised exceptions.

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 clear sections (summary, what it provides, use cases, args, returns, examples, notes, raises). Every sentence adds value; 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?

Tool has only 2 params and no annotations, but description covers all needed context: inputs, behaviors, return formats, error handling, and practical examples. Output schema implied but description compensates with thorough explanation.

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 detailed semantics: vendor and product are case-insensitive, lowercase, with examples. It explains the dual behavior when product is omitted (returns product list vs CVEs).

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 for CVEs by vendor and optionally product. It distinguishes from sibling tools like cve_search_by_id and the many other tools focused on different resources.

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 common use cases and examples, and explains when to omit product to browse products. Does not explicitly contrast with alternatives like cve_database_status or cve_search_by_id, but context is sufficiently 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, so the description carries the full burden for behavioral transparency. It mentions possible exceptions (RuntimeError, ValueError) but does not disclose whether the operation is read-only, idempotent, or any side effects. This is adequate but not comprehensive.

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 sections (Args, Returns, Common Use Cases, Raises) and front-loads the key purpose. Some verbosity exists, especially in the returns section which lists many fields, but it is organized and readable.

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 having only one parameter, the description provides extensive return field documentation and common use cases. The output schema likely covers the return structure, so the description complements it fully. Context is complete for this tool's intended use.

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 defines alert_id as 'The unique identifier of the alert (string)', which adds minimal semantic value beyond the schema type. With 0% schema description coverage, the description should compensate, but it only restates the parameter name and type without examples or format details.

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

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

The description clearly states the tool retrieves detailed information about a specific alert by ID. It distinguishes from siblings like list_alerts, search_alerts, get_alert_history, and get_alert_notes by focusing on a single alert's comprehensive 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?

Common use cases are provided (incident investigation, enrichment, tracking, evidence collection), giving clear context for when to use this tool. It does not explicitly mention when not to use it or alternatives, but the sibling tools (list_alerts, search_alerts) are sufficiently differentiated by the description.

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 fully bears the burden of behavioral disclosure. It explains pagination, chronological order, event types, and raises RuntimeError/ValueError. It is transparent about output structure but lacks details on rate limits or authentication needs.

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 (args, returns, common event types, use cases, raises). It is front-loaded with purpose, and every sentence adds value without being verbose.

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 sibling tools, the description is complete. It details the output schema (edges, pageInfo, event objects), event types, use cases, and error conditions, leaving no obvious gaps for an agent to misinterpret.

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 0% description coverage, so the description must add meaning. It does so excellently by defining each parameter: alert_id as unique identifier, first with range (1-100) and default 10, after as optional pagination cursor. This goes 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 explicitly states it retrieves the complete audit history and timeline for an alert, providing a full audit trail. It distinguishes from sibling tools like get_alert and get_alert_notes by focusing on chronological events, not just the alert itself or notes.

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 lists common use cases such as compliance auditing and investigation timeline reconstruction, offering clear context for when to use the tool. However, it does not explicitly state when not to use it or provide alternative tools for specific scenarios.

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 the tool retrieves a report, returns markdown format and verdict, may return None if no report exists, and raises RuntimeError or ValueError for errors. This provides sufficient behavioral insight without 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 one-line summary, followed by detailed explanation, Args, Returns, Common use cases, Note, and Raises. It is front-loaded with the key purpose, concise, and 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 single parameter and the presence of an output schema, the description adequately explains the return format (markdown report and verdict) and error conditions. It covers common use cases and potential None result. However, it could be slightly more precise about alert_id format, but overall complete for agent decision-making.

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 one required parameter (alert_id) with 0% coverage. The description minimally describes it as 'The unique identifier of the alert' in Args. While adequate given only one parameter, it lacks additional context like format, examples, or validation rules that would aid agent 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 clearly states the action 'Get', the resource 'agentic auto-investigation report', and its association with an alert. It distinguishes itself from sibling tools like 'get_alert' and 'purple_ai' by specifying the report's content (analysis findings, evidence, conclusions, verdict).

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 a 'Common use cases' section listing explicit scenarios, such as reviewing the investigation summary or retrieving past reports. It also notes when the tool returns None (no report exists). However, it lacks explicit guidance on when not to use this tool or comparisons to alternatives like 'get_alert' or 'purple_ai'.

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: returns a list of notes with field details, ordering by newest first, empty array if no notes, and raises RuntimeError and ValueError. This exceeds the burden.

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 (Args, Returns, Use Cases, etc.), but slightly verbose for a one-parameter tool. Front-loads main purpose effectively.

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?

Highly complete given low complexity: explains return format, ordering, error handling, and use cases. Output schema exists, so return value details are not required but are provided.

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 description must compensate, but it merely restates 'The unique identifier of the alert' without additional context (e.g., format, length, constraints). Adds minimal value beyond the schema.

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

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

The description clearly states the tool retrieves all notes and comments for an alert, using specific verbs like 'Get all notes and comments'. It distinguishes itself from sibling tools like get_alert and get_alert_history.

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 explicit guidance on when to use (e.g., investigation, collaboration) and suggests checking alert.noteExists to avoid unnecessary calls. Implies when not to use (if no notes exist).

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 describes return format (JSON string), error types, and field exclusion behavior. It does not mention authentication or rate limits, but for a read-only tool, it's sufficiently 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 well-structured with Args, Returns, Raises sections. It is front-loaded with the main purpose. Every sentence is informative and adds value, with no redundancy or 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?

Given the low schema coverage and presence of output schema, the description is comprehensive. It explains both parameters, return format, error types, and default behavior. It covers all relevant aspects for an agent to use 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?

Schema coverage is 0%, so description compensates fully. It explains item_id as unique identifier and fetch_fields in detail: presets with field counts, custom field list, default value, camelCase keys. Adds significant 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 'Get detailed information about a specific managed asset in SentinelOne by ID.' It uses specific verbs and resources, and distinguishes from sibling tools like list_inventory_items and search_inventory_items.

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 what the tool does and the purpose of parameters, including presets. It implies usage for single-item retrieval but lacks explicit when-not-to-use or comparison with alternatives. Clear enough for 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 full burden. It details the comprehensive data returned, raises RuntimeError and ValueError, and does not mention any destructive side effects. This provides adequate transparency for a read-only retrieval 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 verbose, listing many return fields in detail. While structured with sections (Args, Returns, Use Cases, Raises), it is longer than necessary. Front-loading is good but could be 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?

The description is extremely detailed, covering all aspects of the tool's response and use cases. Since an output schema exists, the description need not explain returns, but it does so thoroughly, making it comprehensive.

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 compensate. It names the parameter 'misconfiguration_id' and states it is a unique identifier (string). This adds sufficient meaning beyond the schema, which only defines type and requirement.

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 detailed information about a specific misconfiguration by ID, using the verb 'get' and resource 'misconfiguration'. It distinguishes from siblings like list_misconfigurations (list) and search_misconfigurations (search) by specifying a single entity 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 includes common use cases such as security posture assessment and compliance auditing, implying when to use. However, it does not explicitly state when not to use or provide direct alternatives, though the sibling list makes 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?

No annotations are provided, so the description carries the full burden. It details return format and errors, but does not explicitly state the read-only nature, authorization needs, or other behavioral traits.

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 headings and sections, though slightly long; every section adds value. Could be marginally 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?

Given no annotations, the description is highly complete: covers return format, event types, common use cases, errors, and pagination. Output schema is described in detail.

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 compensates with detailed Args section explaining each parameter's purpose, constraints (e.g., first: 1-100, default 10), and optionality.

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 the complete audit history and timeline for a misconfiguration, distinguishing it from sibling tools like get_misconfiguration or get_misconfiguration_notes.

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?

Common Use Cases section provides explicit contexts for using the tool (compliance auditing, investigation, etc.), but does not explicitly mention when not to use it or alternatives.

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 states notes are ordered newest first and lists raised errors, but does not disclose read-only nature or authentication requirements. Still appropriate for a simple getter.

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 sections (Args, Returns, Common Use Cases, Raises), front-loaded with the core purpose, and contains no superfluous 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 (one parameter, output schema exists), the description covers purpose, argument, return format, common scenarios, and errors comprehensively.

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 thoroughly explains 'misconfiguration_id' as 'The unique identifier of the misconfiguration.' This single parameter is clearly defined.

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 verb 'Get' and resource 'notes and comments' are clearly stated. The description distinguishes from sibling tools like get_misconfiguration and get_misconfiguration_history by focusing specifically on notes.

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?

Common use cases are provided (remediation, collaboration, audit trail, etc.), but there is no explicit guidance on when not to use this tool or how it compares to alternatives like get_misconfiguration.

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 should disclose behavioral traits (e.g., read-only nature, side effects) but only states the purpose, leaving the agent uninformed about safety or 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 very brief (one sentence) but lacks structure and detail, making it concise at the expense of completeness.

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 an output schema being present, the description omits any explanation of the returned timestamps or how the 9 optional parameters should be used, leaving significant gaps for effective agent usage.

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%, yet the description adds no parameter-level detail beyond the schema's self-explanatory names, failing to clarify how parameters combine to form a time range.

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 generates 'time range timestamps' for a specific use in PowerQuery analytics, differentiating it from sibling tools like iso_to_unix_timestamp which handles single conversions.

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 such as iso_to_unix_timestamp, nor are there any context hints for its appropriate application.

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 fully covers behavioral traits. It details return fields, error types (RuntimeError, ValueError), and mentions retrieving comprehensive data. However, it could mention idempotency or caching, but overall it is 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 lengthy and repeats return field details that are already provided by the output schema (context signals indicate output schema exists). It could be more concise by omitting the verbose return schema listing.

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 (single parameter, straightforward get operation), the description is complete. It covers purpose, parameters, return structure, use cases, and errors. No gaps are apparent.

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 only parameter 'vulnerability_id' is described as 'The unique identifier of the vulnerability (string).' This adds meaning beyond the schema, which has no description. For a single parameter, this is sufficient and clear.

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 detailed information about a specific vulnerability by ID.' It uses a specific verb 'get' and identifies the resource 'vulnerability', distinguishing it from sibling tools like 'list_vulnerabilities' and 'get_alert'.

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 'Common Use Cases' that imply when to use it, but it does not explicitly state when not to use it or provide alternative tools. The context is clear but lacks exclusionary 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 must fully disclose behavior. It explains that the tool returns a paginated chronological list, details event types, mentions errors (RuntimeError, ValueError), and describes the return structure. It does not mention authentication, rate limits, or side effects, but as a read-only history tool, these are less critical. The disclosure is adequate for safe invocation.

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 (Args, Returns, Common Event Types, Common Use Cases, Raises). It is somewhat verbose but each section adds value. The main purpose is front-loaded in the first line. Minor redundancy could be trimmed, but overall it is effective 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?

Although no output schema was provided in the input (context says it exists), the description includes a detailed 'Returns' section outlining the structure, event types, and pagination metadata. It also covers common use cases and error conditions. This completeness compensates for missing schema and ensures the agent can understand the tool's 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?

The input schema defines three parameters with minimal constraints. The description adds critical context: 'vulnerability_id' is the unique identifier, 'first' has a range of 1-100 (default 10), and 'after' is a pagination cursor from previous response. Schema description coverage is 0%, so the description carries full burden and provides complete semantic information.

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 function: 'Get the complete audit history and timeline for a vulnerability.' The verb 'Get' and resource 'vulnerability history' are specific. The name and description distinguish it from sibling tools like get_vulnerability (current state) and get_alert_history (different resource).

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 a 'Common Use Cases' section listing compliance auditing, investigation, etc., which implies when to use the tool. It does not explicitly exclude alternatives, but the context of resource-specific history is clear. No direct comparison with siblings is provided, but the use cases guide appropriate usage.

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; description relies solely on text. Describes ordering (newest first) and error raises, but does not explicitly state read-only nature or authorization 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?

Well-structured with sections (summary, args, returns, use cases, raises). Slightly verbose but each section 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?

Covers input, output fields with examples, common uses, and errors. Output schema exists but description enriches it. Complete for a single-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 has 0% description coverage; description only says 'unique identifier', lacking format or examples. Minimal addition 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?

Clear verb 'Get' and resource 'notes/comments associated with vulnerability'. Distinguishes from sibling tools like get_vulnerability and get_alert_notes.

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?

Lists common use cases and input parameter, but does not explicitly state when not to use or compare to alternatives like get_misconfiguration_notes.

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: it returns milliseconds, converts timezones automatically, assumes UTC if omitted, and raises ValueError for invalid input. This is comprehensive and 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 well-structured with sections like Args, Returns, Examples, and Notes, making it easy to scan. However, it is somewhat lengthy; a more streamlined version could retain all value while being 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?

Given the tool's simplicity (one parameter, no annotations) and the presence of an output schema (though not shown), the description covers all necessary aspects: purpose, usage, behavior, and examples. It is complete for an AI agent to use 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?

The schema has 0% description coverage, but the description compensates thoroughly by detailing the parameter 'iso_datetime' with format, examples, and timezone handling. This adds significant 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 the tool's purpose: 'Convert an ISO 8601 datetime string to a UNIX timestamp in milliseconds (UTC).' This is specific and distinguishes it from siblings like get_timestamp_range, which likely handles range queries rather than single conversions.

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 explicit usage guidance, such as providing datetime in the user's timezone and not converting manually. It lists common use cases but does not mention when not to use the tool or alternatives, which would elevate it to 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 provided, the description fully discloses behavioral traits: pagination mechanism (cursor-based, sequential only), performance notes (use minimal fields for intermediate pages), the conditional inclusion of 'dataSources' based on the 'fields' parameter, and error handling (raises RuntimeError/ValueError). No contradictions exist.

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 lengthy but well-organized with sections, bullet points, and examples. Every part adds value, though some redundancy (e.g., repeating 'dataSources' behavior) could be trimmed slightly without losing clarity.

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 4 parameters, no annotations, and an output schema (present but not detailed), the description is exceptionally complete. It covers pagination flow, performance optimization, parameter behavior, return structure, and error cases, leaving no ambiguity for the agent.

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

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

Despite 0% schema description coverage, the description thoroughly explains each parameter: 'first' (range, default), 'after' (pagination cursor usage), 'view_type' (all options detailed), and 'fields' (JSON string, behavior, available fields with examples). This far exceeds the bare schema information.

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 'List alerts with pagination and filtering capabilities', using a specific verb ('list') and resource ('alerts'). It distinguishes from sibling tools like 'search_alerts' by noting that this tool provides basic filtering, while the sibling handles advanced filtering.

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 advises when to use this tool versus 'search_alerts' (e.g., 'For advanced filtering by severity, status, time ranges, etc., use search_alerts instead'). It also lists common use cases (dashboard feeds, assignment-based distribution, bulk processing, queue management), providing clear context for selection.

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 format (JSON string, camelCase keys, excluded null values), pagination behavior (limit/skip), and detailed error types (ValueError, InventoryAuthenticationError, etc.). Also mentions performance trade-offs for fetch_fields options.

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?

Description is well-structured with summary, usage guidance, parameter details, returns, and raises sections. It is front-loaded with the core purpose. While verbose, each sentence adds value and there is 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 4 parameters and no annotations, the description covers all relevant aspects: pagination, filtering, field selection, output format, and exceptions. The output schema exists (context signal), so return details are adequate. Minor gap: no mention of rate limits or authentication prerequisites beyond error types.

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 fully explains all 4 parameters. limit, skip, and surface are clearly defined with ranges and enum options. fetch_fields is exceptionally detailed with preset names, field counts, and syntax examples. This adds significant meaning beyond the schema's type/defaults.

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 'List managed assets in SentinelOne with pagination and optional filtering,' specifying the exact verb and resource. It distinguishes from sibling tools like get_inventory_item (single item) and search_inventory_items (different query mode).

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 says 'Use this tool to browse SentinelOne managed assets' and provides parameter context like surface filter and fetch_fields options. However, it does not explicitly exclude cases where alternative tools (e.g., get_inventory_item for a single item, search_inventory_items for complex filters) would be more appropriate.

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

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

Despite no annotations, the description fully discloses pagination behavior, parameter effects, return values, error types, and usage patterns.

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, Returns, Raises, Use Cases, and Example sections. Every sentence adds value; appropriate length for the detail required.

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 all aspects: pagination, filtering, field selection, common use cases, errors, and return format. Completeness is high despite output schema existence.

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 extensive detail beyond schema: first range, after cursor usage, view_type enum values, fields JSON string with available fields and examples.

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 lists misconfigurations with pagination and view filtering, and distinguishes from search_misconfigurations for advanced filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use this tool vs search_misconfigurations, provides common use cases, and includes a pagination example.

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, but description covers pagination, parameter constraints, and error types. Does not mention side effects, but it's 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?

Well-structured with clear sections, though lengthy. Every section 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?

Covers all aspects: parameters, pagination logic, return format, use cases, and errors. Output schema exists, but description provides necessary context.

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 explains each parameter in depth, including fields with available subfields and examples.

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

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

Clearly states it lists vulnerabilities with pagination, and distinguishes from search_vulnerabilities for advanced filtering.

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 list_vulnerabilities vs search_vulnerabilities, and provides 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 fully covers behavioral traits: it explains the query language, the need for exact execution, timeout of 5 minutes, default time range, and proper handling of empty results. This gives the agent comprehensive understanding of the tool's behavior 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 verbose but well-structured: it starts with a clear purpose, explains the language, gives important usage notes, then parameter details and guidelines. While every sentence is informative, it could be slightly more concise without losing clarity. However, the structure is logical and easy to follow.

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 (a custom query language) and the lack of parameter descriptions in the schema, the description is exceptionally complete. It covers purpose, language distinction, parameter formatting, time range handling, error behaviors, and even references a sibling tool (purple_ai) for query generation. An output schema exists, so return values need not be described.

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 no descriptions for parameters (0% coverage), but the tool description provides extensive details: for query, it explains it is a PowerQuery string; for start_datetime and end_datetime, it gives ISO 8601 format with multiple examples and timezone requirements. It also provides time range guidance, which adds significant meaning beyond the raw 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 specifically states 'Execute advanced PowerQuery analytics on data in SentinelOne's Singularity Data Lake for complex threat hunting and data analysis,' clearly identifying the verb, resource, and scope. It also distinguishes PowerQuery from Microsoft PowerQuery and Splunk SPL, differentiating it from sibling tools like list_alerts or cve_search_by_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?

The description explicitly instructs to use purple_ai() to generate queries, advises to run user-provided queries exactly, and provides time range guidelines and empty result handling. This clearly indicates when to use this tool vs. alternatives and how to use it correctly.

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: it generates and explains PowerQueries but does not run them, understands natural language, and returns answers. It also warns against instructing it to 'generate a PowerQuery' directly, revealing an important behavioral constraint.

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 (capabilities, limitations, query guidance, examples) and front-loads the purpose. While somewhat lengthy, every section earns its place, though minor trimming could improve conciseness.

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 of the tool (interactive cybersecurity assistant), the description is comprehensive. It covers capabilities, limitations, query format, examples, and guidance. The presence of an output schema also reduces the need to describe return values.

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 'query' has no description in the input schema (0% coverage), but the description extensively explains what kinds of queries to ask, provides examples, and offers tips for effective questions, adding substantial 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 that purple_ai is a cybersecurity assistant that helps investigate threats, generate PowerQueries, and answer questions. It lists specific capabilities and distinguishes itself from sibling tools like powerquery (which runs queries) and alerts 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?

The description explicitly states when to use the tool (asking natural language questions about threats, generating PowerQueries) and when not to (accessing alerts, modifying configs). It provides guidance on how to ask good questions with examples and tips, and contrasts with the powerquery 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?

With no annotations, the description fully discloses behavior: returns filtered list, raises RuntimeError/ValueError, default values, pagination via after cursor, totalCount behavior when first=1, and performance implications of fields. 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 long but well-structured with clear sections (summary, Args, Performance Note, Returns, Raises, Examples). Every sentence adds value, and the details are necessary given the complexity of filtering. Front-loaded with the core 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?

Given the complexity (5 parameters, nested filters), the description covers all necessary aspects: usage, parameter details, examples, error handling, and references to sibling tools. The presence of an output schema means return value details are not required, but the description still mentions the return format.

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 compensates comprehensively. It explains each parameter in detail: filters (field names, filter types, required keys, examples), first (range and default), after (cursor), view_type (enum values), and fields (JSON string, dataSources behavior). Includes both correct and wrong examples.

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 'Search alerts using advanced filters and criteria.' The verb 'search' with 'advanced filters' distinguishes it from sibling tools like list_alerts, which likely provides a simpler listing. The tool's specific function is immediately apparent.

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?

Extensive guidelines are provided: when to use the 'first' field for count queries, when to use fields for performance, and explicit instructions for datetime conversion using the sibling iso_to_unix_timestamp tool. It also includes correct and incorrect examples, helping the agent avoid common mistakes.

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 AND logic for filters, field case and omission, pagination via skip/limit, and raises specific exceptions. It does not mention rate limits or auth requirements beyond error types, but the behavioral traits are well-covered for a search 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 long but well-structured with clear sections (Args, Returns, Raises) and bullet points. It is front-loaded with the purpose and direct URL, making key info accessible. While some details could be trimmed, the organization compensates for length.

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 4 parameters with 0% schema coverage, a complex filters parameter, and an output schema that likely describes return fields, the description provides sufficient context: filter syntax, pagination, field presets, error types, and a workflow for date conversion. It is complete for an agent 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?

Schema description coverage is 0%, so the description must add all meaning. It does so comprehensively: filters parameter is explained with multiple filter types (standard, contains, range, ID, negation, combination) and examples. limit, skip, and fetch_fields are detailed with presets and custom lists, plus a timestamp conversion workflow. This far exceeds the schema's null/default information.

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 for managed assets in SentinelOne using REST API filters. It specifies the resource type (managed assets) and distinguishes from sibling tool list_inventory_items by noting the latter supports surface-specific filtering. The verb 'Search' and the target domain 'inventory items' are 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 explicitly guides when to use this tool vs. the sibling list_inventory_items for surface-specific filtering. It provides extensive filter examples and workflow for timestamp conversion, but does not explicitly state when not to use it or list all alternatives. However, the guidance is clear and actionable.

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

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

No annotations are provided, so the description carries the full burden. It discloses the filter syntax, limits (50 filters, 100 values), unsupported INT filters, and error types (RuntimeError, ValueError). It does not explicitly state the tool is read-only, but the search context implies it. Missing explicit read-only mention prevents a 5.

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 highly detailed but overly long, with extensive lists of field names and examples. While well-structured with sections (Args, Valid Field Names, Filter Types, etc.), it could be more concise. Some redundancy exists (e.g., repeated emphasis on flat camelCase names).

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 (not shown but known), the description covers all necessary aspects: parameters, error conditions, usage patterns, and references to sibling tools. It is complete for a complex filtering tool, addressing edge cases like date format conversion and secretValidity constraints.

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 thoroughly explains all five parameters: 'filters' (detailed JSON structure with examples), 'first' (default 10, range 1-100), 'after' (cursor for pagination), 'view_type' (ALL, CLOUD, etc.), and 'fields' (examples and performance note). This adds substantial 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 tool's purpose: 'Search misconfigurations using advanced filters and criteria.' It distinguishes itself from siblings like 'list_misconfigurations' (referenced in the fields section) and 'get_misconfiguration' by emphasizing advanced filtering 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 provides detailed usage guidance, including when to use specific filter types, field naming conventions, and performance tips (e.g., using minimal fields for paging). It references 'list_misconfigurations' for available fields, helping users decide between 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 carries full burden. It discloses that the tool raises RuntimeError or ValueError on failure, returns JSON, and has limitations (no INT filters). It does not explicitly state that the operation is read-only or idempotent, but that is implied by its search nature. Overall, it clearly explains the filtering behavior and error conditions.

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 long but well-structured with clear sections (Args, Returns, Raises, Examples), bullet points, and code formatting. It is front-loaded with the main purpose. Some redundancy exists (e.g., repeated prefix explanations), but the complexity of the filter system justifies the length. Minor trimming could improve conciseness without losing clarity.

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 (4 parameters, 0% schema coverage, no annotations) and the existence of an output schema, the description is remarkably complete. It covers all parameters with extensive detail, includes examples, notes about timezone conversion, performance advice, and error conditions. The output schema covers return values, so no additional explanation needed there.

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 fully explain parameters. It does so exhaustively: 'filters' gets a multi-page guide with valid field names, filter types, examples, and special cases. 'first' and 'after' are described with defaults and pagination. 'fields' lists available fields and sample JSON arrays. The description adds immense 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 'Search vulnerabilities using advanced filters and criteria.' It names the verb 'search' and resource 'vulnerabilities.' It distinguishes itself from sibling tools like list_vulnerabilities by emphasizing advanced filtering capabilities, and from other search tools like search_alerts by specifying the domain. The extensive detail on filter structure reinforces purpose clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use filters, how to structure them, and includes examples of correct and incorrect usage. It advises using iso_to_unix_timestamp for datetime conversions, notes that product/vendor only support STRING filters, and gives a performance tip for paging. It also mentions the maximum filter limits and valid field names, leaving little ambiguity.

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. Details API key requirement, rate limit differences, error raises, and response format for not-found. Discloses read-only nature implicitly.

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 (What, Use Cases, Args, Returns, Examples, Notes, Raises), but slightly verbose with redundancy (e.g., repetition of domain intelligence details). Still earns its place.

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

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

Given single parameter and existence of output schema, description covers purpose, usage, behavioral nuances, parameter details, examples, prerequisites, and error handling. 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?

Only one parameter 'domain'. Schema description coverage is 0%, but description provides clear definition with example format and additional examples section, fully compensating.

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 'Get threat intelligence for a domain' with specific verb and resource. Differentiates from sibling threat intel tools (by hash, IP, URL) by specifying domain focus.

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?

Lists common use cases (investigate domains, research C2, etc.) and notes outcomes like 'not found returns structured JSON'. Lacks explicit when-not-to-use or direct comparison to alternatives, but 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 provided, so the description carries full burden. It discloses caching behavior, API key requirement, rate limit differences, not-found response structure, and possible exceptions (ThreatIntelligenceClientError, RuntimeError). All behavioral traits are transparently documented.

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 sections (intro, what it provides, use cases, args, returns, examples, notes, not found, raises). It is front-loaded with purpose. A bit lengthy but every section adds value; 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?

Given the presence of an output schema, the description still explains the return structure in detail (detection stats, file metadata, etc.), covers prerequisites (API key), error conditions, and not-found case. It is fully complete for an agent to invoke this 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?

Schema coverage is 0%, but the description thoroughly defines the hash_value parameter: accepted formats (MD5, SHA1, SHA256), case-insensitivity, and provides examples. 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 retrieves threat intelligence for a file hash from VirusTotal/Google Threat Intelligence. It distinguishes from siblings by specifying hash-based lookup, while sibling tools handle domains, IPs, URLs, etc. The specific verb 'Get' and resource 'threat intelligence for a file hash' are 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 common use cases (incident response, threat hunting, etc.) and explicit notes: requires valid API key, file must have been previously submitted, results are cached, and private API keys have higher rate limits. It implicitly advises when to use this tool versus siblings like threat_intel_get_file_behavior for behavioral analysis.

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; description covers key behaviors: requires API key, returns historical data, private IPs may lack data, structured JSON for missing IPs, and error types. Does not mention mutation/read nature explicitly but implied.

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, but verbose with many bullet points. Some redundancy (e.g., reputation mentioned twice). Front-loads purpose effectively but 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 simple input (1 parameter) and existence of output schema, the description is very thorough: covers input, output fields, notes, errors, and examples. No gaps identified.

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 one parameter (ip_address) is described clearly with type and examples (IPv4/IPv6), compensating for 0% schema description coverage. The description adds context beyond the schema's property definition.

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 threat intelligence for an IP address from VirusTotal/GTI. It lists features and use cases, but does not explicitly differentiate from sibling tools like threat_intel_by_domain or threat_intel_by_hash.

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 explicit use cases (e.g., investigate suspicious IPs, research C2 servers) and notes on limitations (private IPs, API key requirements). However, no guidance on when not to use or alternatives among sibling threat intel 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 provided, but description thoroughly discloses API key requirement, potential URL scanning, privacy implications, rate limits, return structure for not found, and errors.

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?

Long but well-organized into clear sections (what it provides, use cases, args, returns, examples, notes). Front-loaded with core purpose, though 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?

Complete for a single-parameter tool with output schema existence. Describes output structure, common use cases, error conditions, and privacy implications.

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?

Only one parameter (url) with 0% schema coverage. Description adds valid URL format requirement and provides examples, significantly compensating for 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 'Get threat intelligence and reputation information for a URL' and distinguishes from sibling tools like threat_intel_by_domain and threat_intel_by_hash by specifying it's for URLs.

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 common use cases (email security, web filtering, etc.) and examples, but doesn't explicitly say when not to use or compare to siblings for URLs vs domains.

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 fully discloses behavioral traits: requires API key, supports only SHA256, returns up to 50 reports, behavior in controlled sandbox, private key access, and structured response on missing data. 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 sections and bullet points, front-loaded with a summary. While somewhat verbose, every section adds value and no information is redundant. Slightly long but appropriate for the 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?

Given the tool has an output schema (though not shown), the description still covers return format with field lists. It includes all necessary sections: purpose, parameters, returns, examples, notes, and use cases. Complete for a complex threat intelligence 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%, so the description must compensate. It thoroughly explains both parameters: hash_value (SHA256 preferred) and sandbox (optional sandbox name), with examples and formatting details, adding substantial 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 tool retrieves behavioral analysis reports from VirusTotal sandboxes, specifying the action (get), resource (file behavior), and context. It distinguishes from sibling tools like threat_intel_by_hash and threat_intel_get_file_relationships by focusing on sandbox execution details.

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 lists common use cases (malware analysis, detection engineering, etc.) and provides critical usage notes, including when not to use (e.g., not all files have analysis) and an important warning against repeated calls with the same parameters.

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, description fully discloses behaviors: returns up to 100 relationships, not all types available, sandbox execution source, private API keys have more, returns found=false on not found, idempotency warning, and error types. Very comprehensive.

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 and bullet points, but slightly verbose. Could be trimmed without losing key information. Still efficient given the detail needed.

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 all aspects: purpose, parameters with examples, return format, error handling, authentication, and usage constraints. Explains output structure (JSON with relationships and count). No gaps given complexity.

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 property descriptions), but the description compensates fully by specifying hash format (MD5/SHA1/SHA256), relationship_type values with examples, and listing all available types. Adds significant meaning beyond 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 tool retrieves relationships for a file hash from VirusTotal, listing specific relationship types and use cases. It distinguishes from siblings like threat_intel_by_hash by focusing on relationships rather than general file intelligence.

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 explicit use cases such as extracting network IOCs and pivoting from files. Includes important note about not calling repeatedly with same params. Could be improved by mentioning when NOT to use this tool (e.g., use threat_intel_by_hash for general file analysis).

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 fully covers behavior: API key requirements, result limit (10), query complexity, warning against repeated same-parameter calls, and error types. Exceptionally 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?

Well-structured with clear sections (Query Syntax, What this tool provides, Use Cases, Args, Returns, Notes, Raises). Slightly verbose due to extensive examples and use cases, but every part 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?

For a single-parameter tool with no annotations and no schema descriptions, the description covers input (query syntax), output (JSON structure), limitations (10 results, API key), warnings, and errors. 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?

Single 'query' parameter has 0% schema coverage, but description compensates with extensive query syntax examples, covering file types, size, detections, time, behavior, tags, strings, imports, certificates—fully explaining what the parameter accepts.

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

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

Clearly states it searches VirusTotal Intelligence with advanced queries for threat hunting. Distinguishes from sibling tools that search by specific indicators like hash, IP, domain, etc.

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 explicit use cases (threat hunting, malware research, IOC expansion) and when it is essential. Lacks explicit when-not-to-use instructions, but sibling tool differentiation implies it.

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