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

With no annotations provided, the description carries the full burden. It discloses that the tool creates a new control and attaches a check to it, which implies a write operation, but does not specify permissions, idempotency, or side effects like what happens if the parent control doesn't 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 concise and well-structured with separate 'Args' and 'Returns' sections. It is front-loaded with the core purpose and uses minimal but sufficient text, earning its 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 no annotations, the description covers the return values (success, error) and the basic behavior. However, it could include more context about error conditions, duplicate checks, or required permissions. Still, it provides a solid foundation for an agent to understand the tool's function.

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. The 'Args' section adds context for each parameter (e.g., 'parentControlId' is described as the parent control under which the check will be added), which goes beyond the basic type definitions in 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 action: 'Add a new control and a new check to an asset under a specified parent control.' It uses specific verbs and identifies the resources (asset, control, check), distinguishing it from siblings like create_asset_and_check which creates the asset itself.

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

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

The description provides a clear purpose but lacks explicit guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or when not to use it. The context with siblings is not leveraged to differentiate use cases.

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

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

No annotations are provided, and the description only states returns (success, error) without detailing behavioral traits such as idempotency, permissions, or effects of duplicate citations. The typo 'asse' further undermines clarity.

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 of moderate length but includes irrelevant information about asset hierarchy. It could be more concise if focused on the actual citation action.

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

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

Given no annotations and 3 parameters, the description fails to accurately describe the tool's purpose (mismatch with name) and lacks essential context about citations and controls.

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

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

Input schema coverage is 0%, and the description merely repeats parameter names with vague one-line descriptions (e.g., 'Id of the control in asset'). It does not explain formats, constraints, or relationships between parameters.

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

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

The description says 'Create a new asset with an initial control and check structure,' which contradicts the tool name 'add_citation_to_asset_control.' The parameters are about linking a citation to an existing asset control, not creating an asset. This is misleading.

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 given on when to use this tool versus siblings like 'suggest_control_citations' or 'attach_rule_to_control.' The description does not clarify the context of use.

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

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

No annotations are provided, so the description carries the full burden. It explains the effect (adding an identifier to appTags) and the purpose (matching applications to tasks). However, it does not disclose potential side effects like overwriting existing keys, error conditions (e.g., task not found), or authentication requirements. A score of 3 reflects adequate but incomplete disclosure.

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 moderately long but well-organized into sections (purpose, when/not to use, workflow, args, returns). It is front-loaded with the main action. While every section earns its place, the length could be slightly reduced without losing clarity. A score of 4 reflects good structure and efficiency.

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 required parameters, part of a multi-step workflow), the description provides sufficient context: it references the preceding step (prepare_applications_for_execution) and explains the return type. The absence of an output schema is mitigated by stating 'Dict with update status and guidance'. Overall, it is complete for an agent to use correctly.

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

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

The input schema has 0% description coverage, meaning the schema provides no parameter documentation. The description compensates by listing all four parameters with examples and context in an 'Args' section. This adds significant meaning beyond the raw schema, guiding the agent on how to populate them correctly.

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

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

The description clearly states the verb 'Add', the resource 'task's appTags', and the specific action 'unique identifier key-value pair'. It is precise and distinguishes itself from siblings like 'create_control_note' or 'update_control_note', which do not add identifiers to tasks.

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

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

The description explicitly provides 'WHEN TO USE' and 'NOT NEEDED WHEN' sections, including a workflow that references a specific sibling tool (prepare_applications_for_execution). This gives clear context and alternatives, making it easy for an agent to decide when to invoke this 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 behavioral traits: it requires user confirmation for override and evidence creation, performs validation steps, and may fail if preconditions aren't met. However, it doesn't explicitly state side effects or rollback behavior, keeping it from a perfect score.

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 long and detailed, with headers and bullet points for structure. While thorough, it could be more concise; some repetition exists (e.g., 'CRITICAL EXECUTION BLOCKERS' reiterated in workflow). The length may overwhelm the agent, earning a middle score.

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 (multiple preconditions, user confirmations, workflow steps), the description is highly complete. It covers control verification, rule resolution, publish validation, evidence creation, override handling, and return value expectations. An output schema exists but doesn't reduce the need for this rich 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?

The input schema has no descriptions (0% coverage), so the description compensates by explaining each parameter's usage, especially rule_id (UUID or name resolution) and create_evidence (needs user acknowledgment). It adds context about resolution logic, but lacks details on format constraints for assessment_name and control_id.

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

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

The first sentence clearly states 'Attach a rule to a specific control in an assessment,' which provides a specific verb and resource. This differentiates it from sibling tools like 'add_check_to_asset' or 'add_citation_to_asset_control' by focusing on rule-control attachment.

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 critical execution blockers and a detailed workflow, including preconditions like control verification, rule ID resolution, and user confirmations. It mentions using related tools (verify_control_in_assessment, fetch_cc_rule_by_name) for prerequisite steps, offering clear when-to-use and when-not-to-use guidance.

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

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

With no annotations, the description carries the burden of disclosing behavior. It states 'Check publication status', which implies a read-only operation, but does not explicitly confirm no side effects, authentication needs, or rate limits. Minimal but acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at 5 lines, including args and returns. The structure hint could be clearer with formatting, but overall it is efficient without extraneous text.

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 (expected), the description covers input format and return structure. However, it does not address error handling or edge cases, leaving minor gaps for a tool with a single parameter.

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 tool description provides detailed structure: 'app_info structure is [{"name":["ACTUAL application_class_name"]}]' and 'List of application objects to check'. This goes beyond the schema's minimal object type array.

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 'Check publication status for each application', specifying the verb (check) and resource (application publish status). It distinguishes from sibling tools like 'check_rule_publish_status' and 'publish_application' by targeting applications and only checking status.

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

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

The description does not explicitly mention when to use this tool versus alternatives. It implies usage via the action 'check', but lacks explicit context such as 'Use this to verify publication status before publishing' or exclusion of sibling tools.

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

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

Lacks annotations; the description is ambiguous about side effects. The bullet 'If not published → publish the rule' suggests a write operation, but the tool name and return type imply read-only. No disclosure of actual behavior.

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 structured with bullets and an Args section, but includes verbose workflow instructions that are not part of the tool's direct behavior, reducing 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?

Does not describe the return dict structure, error cases, or side effects. For a simple tool with no annotations or output schema detail, the description is incomplete.

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

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

With 0% schema description coverage, the description explains 'rule_name: Name of the rule to check'. This adds basic meaning beyond the schema's type-only definition, but is minimal.

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

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

The description states 'Check if a rule is already published' which is clear, but then includes workflow steps that suggest the tool may also publish or prompt the user, creating ambiguity about the tool's core function.

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 on when to use this tool vs siblings like 'publish_rule' or 'check_rule_status'. The workflow steps imply subsequent actions but do not explicitly differentiate.

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

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

The description thoroughly explains the tool's behavior beyond annotations: it ignores stored status/phase fields, analyzes actual rule structure, auto-detects completion status, calculates progress percentages, and provides resumption guidance. This ensures the agent understands exactly how it operates.

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, with repeated emphasis on auto-inference and a long list of auto-inference logic details. While structured with headings, it could be more concise without losing critical 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 has one parameter and the description explains the output (dict with status information), it covers the essential aspects. The description also addresses resumption use cases, making it sufficiently complete 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?

The only parameter, rule_name, is described simply as 'Name of the rule to check status for.' With 0% schema description coverage, the description adds minimal value but covers the necessary context. For a single string parameter, this is adequate.

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 performs a quick status check on a rule, showing what's collected and missing. It distinguishes itself from sibling tools by emphasizing auto-inference status analysis, making its unique purpose evident.

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 mentions the tool is 'Perfect for resuming in new chat windows,' providing clear guidance on when to use it. However, it does not explicitly state when not to use it or list alternatives, though the unique auto-inference feature implies scenarios where other status tools might not be suitable.

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 extensively discloses behaviors: collects only primitive types, stores in memory, never uploads files, validates types, requires mandatory confirmation, and details the default value workflow. 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 excessively long with repeated information, multiple sections, bullet points, and numbered lists. While structured, it is not concise; many details like the final confirmation workflow are reiterated. Every sentence does not earn its place, reducing efficiency for an AI agent.

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 and lack of annotations, the description covers purpose, parameters, workflows, validation rules, and critical rules comprehensively. It also mentions return type. However, the verbosity slightly hinders quick understanding, but overall completeness is high.

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 compensate. It lists the four parameters (task_name, input_name, user_value, use_default) in an 'Args' section with brief descriptions, adding meaning beyond the schema. However, the descriptions are somewhat terse and could provide more detail on formats or constraints.

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 collects user input for non-template parameter inputs, specifies it handles primitive types, and distinguishes from sibling tool 'collect_template_input' by explicitly mentioning 'non-template'.

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

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

The description provides clear context for when to use (for parameter inputs) and includes critical rules, but does not explicitly list alternatives or when not to use, though the distinction from template inputs is implied.

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

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

With no annotations provided, the description carries full burden. It discloses validation behavior, file upload condition (only for FILE dataType), mandatory confirmation, and rule updates. It covers error-prone details like JSON array handling and sanitization. Lacks details on error handling or idempotency, but overall 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 lengthy and contains redundancy (e.g., 'Preserved' repeated, 'NEW:' temporal markers). It is well-structured with sections, but could be more concise. Some sentences are wordy (e.g., 'MANDATORY: Task-sequential collection only.' could be shorter). Adequate but not optimally 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 complexity (3 parameters, no annotations, output schema only described in text), the description covers workflow integration, validation requirements, file naming convention, and return values. It includes critical rules and workflow steps. Missing some edge case handling (e.g., validation failure behavior), but overall complete for a template input collection 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 0% description coverage, so the description must compensate. It provides clear semantics for each parameter: `task_name` (task identifier), `input_name` (name of input), and `user_content` (content based on template). It also explains validation formats (JSON/TOML/YAML/XML) and required fields, adding 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 the tool's purpose: 'Collect user input for template-based task inputs.' It provides specific actions (validate, upload, store) and distinguishes from siblings like `collect_parameter_input` and `confirm_template_input` by focusing on template-based inputs. The verb 'collect' and resource 'template input' are precise.

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 context on when to use: 'Called after get_template_guidance() shows template to user' and 'Task-sequential collection only.' It also includes critical rules like 'Never use template defaults' and that it is for template inputs, not parameter inputs. However, it does not explicitly list alternatives or when not to use, so slightly incomplete.

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 thoroughly discloses all behavioral traits: it requires mandatory user prompt, is non-skippable, and details three selection options (standard, extended, both) with specific actions for each. It also mentions chart generation and mapping persistence.

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 contains redundant phrasing (e.g., 'NON-SKIPPABLE' repeated multiple times). It is well-structured with clear sections, but could be more concise. The front-loading of the prerequisite warning is good.

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 (setting up output schema with user interaction), the description is highly detailed and covers all necessary aspects: purpose, prerequisites, user prompt requirement, behavior per selection, validation rules, and follow-up steps. However, the output schema is mentioned but not fully detailed in the description (though context says it exists).

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, and schema description coverage is 100%. The description explains that the tool requires a user choice (a/b/c), which is not a formal parameter but is effectively the tool's input. It adds meaning beyond the schema by describing the three options and their implications.

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 purpose: 'Establish the rule's output schema policy for ComplianceCow and apply any required transformations.' It explicitly positions itself as a mandatory prerequisite that must run before `prepare_input_collection_overview()`, distinguishing it from sibling 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 provides explicit when-to-use guidance: it MUST run first and is non-skippable. It also states when-not-to-use: if not executed or user does not provide explicit choice, the workflow must fail. It names the next tool (`prepare_input_collection_overview()`) and sets the ordering.

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 storing in memory, not uploading files, requiring explicit user confirmation, and the automatic rule update process including steps like fetching current rule structure and calling create_rule(). It lacks details on error handling or idempotency but covers essential 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 sections but is verbose. It repeats information (e.g., confirmation types and storage rules appear in multiple sections). While front-loaded with the main purpose, it could be more concise 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 has 7 parameters (5 required) and an output schema, the description covers behavior, parameter semantics, and output format ('Dict containing stored value confirmation and rule update status'). There are no significant gaps for the tool's 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 description coverage is 0%, so the description must add meaning. The Args section explains each parameter, including constraints like rule_input_name must be one of the rule structure's inputs and explanation is only for JQ/SQL expressions. It adds context beyond the schema, though some parameters like task_name are not elaborated beyond their name.

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 confirms and stores parameter input after user validation. It specifies the action (confirm and store), the resource (parameter input), and the context (user validation). It distinguishes from siblings like 'collect_parameter_input' and 'confirm_template_input' by detailing automatic rule updates and memory storage.

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 it is a 'MANDATORY step before proceeding to next input', providing clear when-to-use guidance. It mentions handling default and final confirmation types and automatic rule updates when rule_name is provided. However, it does not explicitly state when not to use this tool or what alternatives exist, though the sibling list provides context.

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

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

No annotations are provided, so the description carries full burden. It comprehensively discloses the processing flow (file upload for FILE/HTTP_CONFIG, memory storage for others), automatic rule updates (including the step-by-step process), and UI display requirement. It does not mention error handling or permissions, but the detail is sufficient for transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly verbose with multiple sections in ALL CAPS, bullet points, and repetition (e.g., 'NEW' and 'Enhanced' markers). While it contains necessary information, the structure is not concise and could be streamlined to improve 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 tool's complexity (5 parameters, no enums, output schema present), the description covers the processing steps, side effects (automatic rule updates), and UI requirement. It mentions the return type as a dict with file URL/memory reference and rule update status. It lacks error handling details or prerequisites, but is otherwise complete for effective 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 input schema has 0% description coverage for its 5 parameters. The description's 'Args' section adds valuable context: it specifies that rule_name should be consistent across inputs, task_name identifies the task, rule_input_name must match rule structure, confirmed_content is the validated content. This compensates for the schema's lack of descriptions, though not all parameters are equally detailed.

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 'Confirm and process template input after user validation.' It clearly states the verb (confirm/process) and the resource (template input). It distinguishes from siblings like confirm_parameter_input by specifying it's for template inputs, and it details sub-actions like file uploads and memory storage.

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 it is a 'MANDATORY step before proceeding to next input' and mentions it skips confirmation if the user accepts the suggested template. This provides clear context on when to use. However, it does not explicitly compare to sibling tools like collect_template_input or confirm_parameter_input, nor does it specify when not to use.

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

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

With no annotations, the description fully carries the burden. It details the hierarchical creation, parameter constraints (checkName format), and return values. It lacks info on idempotency or error details.

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 bullet under 'Args', front-loaded with main purpose. Minor typo ('asse' instead of 'asset') slightly reduces 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 params, no annotations, and implied output schema, description is quite complete. Covers hierarchy, parameters, and returns. Missing context like authentication or error scenarios.

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. It provides clear meaning for all four parameters, including format constraint for checkName, adding 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 it creates a new asset with a hierarchical control and check structure. It distinguishes from sibling 'add_check_to_asset' by emphasizing creation of the full hierarchy.

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

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

The description implies usage for creating a new asset with initial structure, but does not explicitly state when to use this tool vs alternatives like 'add_check_to_asset'. No when-not or exclusion conditions provided.

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

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

Without annotations, the description fully discloses the dual behavior: preview mode vs. permanent creation. It explains the return values and that the note is attached to a control. It does not mention permissions or side effects, but the core behavioral split is well-covered.

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 detailed but well-organized with sections, bullet points, and formatting. Each part serves a purpose, though it is slightly longer than necessary. The main purpose is front-loaded.

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

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

Given the tool's complexity (confirmation flow, five parameters, detailed return schema), the description is complete. It explains the preview behavior, the creation mode, and the full return object, making it self-sufficient even without annotations.

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, but the description lists all five parameters with their purposes, required status, and the confirm parameter's boolean options. It also explains the return structure, adding significant 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 that the tool creates a documentation note attached to a control, specifying the verb 'create' and the resource 'control note'. It distinguishes itself from siblings like 'update_control_note' and 'list_control_notes' by focusing on creation.

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 the confirmation-based safety flow with confirm=False for preview and confirm=True for persistence. While it does not explicitly mention when to use this tool versus alternatives, the flow is clearly explained.

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 bears full burden for behavioral disclosure. It discloses that the tool creates and saves design notes and returns access details. However, it could be more transparent about side effects (e.g., overwriting) or permissions required, but the provided info is solid.

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, front-loading the main purpose. While slightly verbose with repeated workflow steps, it is organized and clear, earning a high but not perfect score for 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 tool's role in a multi-step workflow, the description effectively explains how it fits: prerequisite calls, user confirmation, and return details. It references sibling tools and provides a comprehensive overview, making it complete for its 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 description coverage is 0%, so the description must compensate. It does so by explaining rule_name as 'Name of the rule' and design_notes_structure as 'Complete Jupyter notebook structure as dictionary', adding meaningful context 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 'Create and save design notes after user confirmation', specifying the verb and resource. It also distinguishes itself from sibling tools like generate_design_notes_preview and fetch_rule_design_notes by outlining the workflow context.

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

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

The description explicitly provides usage guidelines: call fetch_rule_design_notes first to check existence, then proceed after user confirmation of the preview. It also lists the workflow steps, making it clear when to use this tool versus 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?

With no annotations, the description fully details behavior: it handles creation and updates, auto-detects status, has progressive phases, and returns a URL. It does not mention auth needs or rate limits, but covers the primary behavioral aspects comprehensively.

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 long and repetitive, with multiple sections repeating similar information (e.g., progressive saving, phases). While important details are present, conciseness is lacking; it could be streamlined without losing meaning.

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 rule creation, the description is extremely complete. It covers all phases, validation, examples, and requirements. It also references an output schema (though not provided), so return values need not be described. The description leaves no critical gaps.

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

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

The input schema has only one parameter (rule_structure) with 0% schema description coverage. The description compensates extensively by documenting the structure with a YAML example, steps, validation checklist, and all required fields, providing far more meaning than the schema alone.

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

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

The description clearly states 'Create a rule with the provided structure' and elaborates on progressive saving and automatic status detection. It distinguishes from sibling tools (e.g., fetch_rule, publish_rule) by specifying it is for creation and progressive updates.

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 specifies when to call the tool: after planning, after each input collection, and after input verification. It lists pre-creation requirements and workflow steps, but does not explicitly state when not to use it or mention 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?

With no annotations, the description carries full burden. It explains the tool saves the file and returns access details, but does not address potential overwrites, permissions, or side effects. The behavior is adequately disclosed but lacks deeper safety context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections (README CREATION, WORKFLOW, Args, Returns). It is front-loaded with purpose, each sentence adds value, and there is no extraneous information.

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

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

For a creation tool with 2 string parameters and an output schema, the description covers the workflow and return details. It lacks error scenarios (e.g., invalid content), but is largely sufficient for an AI agent to understand the tool's role.

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

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

Input schema has 0% description coverage. The description adds 'Name of the rule' and 'Complete README.md content as string', which clarifies the parameters beyond the schema. This compensates well, though more detail (e.g., format constraints) would improve.

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 'Create and save README.md file after user confirmation.' It explicitly differentiates from the sibling tool generate_rule_readme_preview and update_rule_readme by specifying it is the final step after preview and confirmation.

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

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

The description provides a clear workflow: user must have reviewed preview and confirmed. It implies usage only after preview, but does not explicitly state when not to use or list alternatives. The context 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 exist, so the description carries full burden. It describes intended agent behavior (generating descriptions, blocking creation) rather than actual tool behavior, which may mislead. It adds some context but is more about process than tool capabilities.

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 and repetitive, with multiple bullet sections. It is structured but could be more concise. Several sentences rephrase the same idea of mandatory user approval.

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

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

For a tool with 3 parameters and no enums, the description thoroughly covers the workflow and output. It mentions the return type (dictionary) and provides all necessary context for correct usage, though it overemphasizes process.

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

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

With 0% schema coverage, the description compensates by explaining each parameter: subject as title, description as HTML-formatted and user-approved, priority with valid case-sensitive values (High, Medium, Low). This adds meaningful guidance 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 creates support tickets after user review. It differentiates from sibling tools by emphasizing a strict approval workflow, though no direct sibling comparison is made.

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 mandatory conditions and steps, including when to use (after user approval) and when not to use (without approval). It does not compare to alternatives but offers clear context.

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

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

With no annotations, the description should disclose side effects, permissions, or safety concerns. It mentions displaying a diagram and a confirmation step but does not address reversibility, overwrite behavior, or error cases. The YAML struct hint adds some context, but behavioral traits are insufficiently covered.

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 somewhat verbose, containing redundant statements (e.g., 'Create a new workflow using YAML definition' and 'This function creates a workflow from a YAML specification'). It includes an Args/Returns section and a code block, but could be more tightly written 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 existence of an output schema (not shown), the description reasonably covers the tool's purpose, usage, and parameter format. However, it lacks detail on expected behavior, error states, or prerequisites, and does not fully differentiate from the many sibling tools that exist on the server.

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 provides a YAML struct example and states that the parameter is a 'YAML string defining the workflow structure,' which adds meaning beyond the schema's type-only definition. However, it does not fully specify all YAML fields or constraints, leaving gaps.

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

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

The description clearly states the tool creates a new workflow using a YAML definition, and distinguishes it from the similarly named 'modify_workflow' tool, which handles updates. The verb 'create' and resource 'workflow' are specific, and the YAML format is explicitly referenced.

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: confirm with user before execution and later use 'modify_workflow' for updates. It outlines a multi-step process for workflow creation, which helps the agent decide when to use this tool, though it could also note when not to use it.

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: it validates payload item types, requires explicit user confirmation, and returns a success or error message. It explains that when confirm=False, it returns a preview without creating the event. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a purpose statement, usage instructions, and parameter list. It is front-loaded with the key confirmation requirement. While it is fairly detailed, it could be slightly more concise, but the structure 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 complexity (6 parameters, nested schema), the description covers the main behavior and all parameters. It explains the confirmation workflow and validation. The return value is vaguely described as 'Success or error message,' but since the context indicates an output schema exists, this is acceptable. It adequately supports an agent's 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 description explains all six parameters in the Args section, adding meaning beyond the schema. For example, it lists the allowed types for payload items, which the schema references via $defs. Schema coverage is 0%, so the description carries the full burden and does so effectively.

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: 'Create a Workflow Catalog Custom Event.' It uses a specific verb (create) and resource (Workflow Catalog Custom Event), and the tool name is self-explanatory. Among siblings, there is no other creation tool for custom events, so differentiation is clear.

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 usage guidelines: 'Show a preview of the event configuration and ask for user confirmation before proceeding. Only create the event after explicit confirmation from user (confirm=True).' It instructs the agent to first call with confirm=False to show a preview, then only proceed with confirm=True after user acceptance. This clearly defines when and how to use the tool.

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

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

No annotations exist, so the description carries full burden. It discloses deletion and return values but omits important behavioral traits like irreversible effects, required permissions, or error handling for non-existent schedules.

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 short and front-loaded. However, the structured Args/Returns format adds redundancy given the schema and presumed output schema. Some fluff 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?

For a simple delete tool with one parameter and an output schema, the description is adequate but lacks explanation of edge cases (e.g., idempotency, what happens if schedule doesn't exist).

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 'scheduleId' has 0% schema description coverage. The description adds 'ID of the schedule to delete,' which is redundant with the parameter name and adds no additional meaning (e.g., format, source, or validation).

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

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

The description clearly states the action (delete) and the resource (assessment schedule). It distinguishes from sibling tools like list_asset_schedules and schedule_asset_execution.

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 on when to use this tool vs alternatives, nor prerequisites like schedule existence or permissions. The description provides no context for decision-making.

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 that this tool modifies system state, requires explicit user confirmation, and can only trigger one action at a time. It explains the return value (triggered action id) and references the need to fetch action binding IDs, providing clear behavioral expectations.

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 and contains repetition (e.g., user confirmation is mentioned multiple times). It includes a lengthy workflow and multiple warnings that could be condensed. The essential information is present but not optimally 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 complexity (7 parameters, no schema descriptions, no annotations, output schema exists), the description covers the workflow, safety, level-specific parameters, and return value. It does not address error handling or non-standard scenarios, but it is largely sufficient 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 the description must compensate. It explains which parameters are needed for each level (control, evidence) and notes that inputs are optional and action-specific. It also describes how to obtain actionBindingId. However, it lacks detailed semantics for each parameter (e.g., how to derive assessmentId, assessmentRunId) beyond level differentiation.

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 executes actions on assessment runs at different levels (assessment, control, evidence). It specifies the resource ('action on assessment run') and verb ('execute or trigger'), and distinguishes itself from sibling tools like fetch_assessment_available_actions by referencing them in the workflow.

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 an explicit step-by-step workflow, including prerequisites (fetch available actions), user confirmation requirements, and parameter necessity by level. It includes strong warnings not to execute without user consent and instructs to prompt for required fields and input modifications.

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

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

The description lacks annotations, so it carries the full burden of behavioral disclosure. It explains the database structure and query patterns but does not warn that the tool can execute write queries (Cypher includes CREATE, DELETE, etc.), potentially leading to unintended data mutation. It also omits permission requirements or error handling details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement, nested details, and examples. It is somewhat lengthy but every section adds value. It could be slightly more concise by removing redundant phrases.

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 minimal input schema and no annotations, the description provides a comprehensive overview of the tool's behavior, database context, and return format. It is mostly complete but could benefit from mentioning write operation implications and more detailed error handling.

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

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

With 0% schema coverage for the single parameter 'query', the description fully compensates by providing query guidelines, database schema details, and multiple example queries. This adds critical meaning beyond the bare schema 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 the tool executes a Cypher query and transforms the result to a human-readable format. It specifies the database type (Neo4j) and provides extensive context on database structure, making the purpose unambiguous and distinct from sibling 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 provides query guidelines, example queries, and hints for traversing hierarchies. While it does not explicitly compare to alternative tools or state when not to use it, the context given is sufficient for an agent to understand appropriate usage 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 provided, the description carries the full burden of behavioral disclosure. It details the execution workflow, application matching logic, output display process, and optional publication. It also specifies critical data formatting rules for 'rule_inputs'. However, it does not explicitly mention authentication requirements or side effects like data mutation.

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 excessively long, running multiple hundreds of words with a full workflow that includes UI display requirements and step-by-step instructions. While it is structured with sections and formatting, it could be significantly trimmed without losing essential 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 complexity (rule execution with application orchestration and output handling), the description covers all necessary aspects: prerequisites, configuration options, matching logic, parameter specifications, post-execution steps, and optional publication. The mention of a return value (Dict with execution results) complements the output schema richness.

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 'Args' section in the description adds substantial meaning to each parameter. It explains the complex structure of 'rule_inputs' and 'applications' beyond the generic object type, including constraints like the 'defaultValue' requirement. The explanation is thorough but verbose.

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 indicates that the tool executes a rule, but the purpose is embedded in a lengthy workflow narrative rather than stated concisely upfront. The verb 'execute' and resource 'rule' are explicit, and it distinguishes from siblings like 'execute_task' by specifying the scope.

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

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

The description provides extensive usage guidance including mandatory prerequisite steps and post-execution actions. However, it does not explicitly contrast with sibling tools like 'execute_task' or 'publish_rule', and when-not-to-use scenarios are implied rather than stated.

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?

Given no annotations, the description carries full behavioral burden. It clearly states the tool executes with real data, handles dependencies, and shows results. However, it could explicitly mention that execution may trigger real-world actions (e.g., data modification) beyond what the name implies, but the emphasis on 'real data' and the execution flow strongly implies mutation.

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-structured with clear sections (execution context, dependency handling, configuration, flow) and bullet points. It is front-loaded with the core purpose. While every sentence adds value, it could be slightly more concise, but the complexity warrants the 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?

The description covers all aspects: input parameters, dependency logic, error handling, application configuration, execution flow, and return value format. Given the tool's complexity and the presence of an output schema, it is complete and leaves no significant gaps for an AI 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?

With 0% schema description coverage, the description fully compensates by detailing parameter formats: task_name as a string, task_inputs as key-value pairs, and application with appName, credentialType, etc. It provides a complete request body example and explains optionality based on appType, adding critical meaning beyond the minimal 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: executing a specific task with real data after input collection. It emphasizes real data vs. sample, sequential execution, and dependency handling, distinguishing it from sibling tools like execute_rule or fetch_task_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 provides explicit guidance on when to use the tool ('MUST be called after collecting inputs'), how to handle dependencies (execute previous tasks automatically, use real outputs), and when not to use it (do not generate synthetic data). It also explains fallback on failure and the minimal execution principle.

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

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

The description does not reveal any behavioral traits beyond the basic operation. No details on performance, pagination, data size, or side effects. Since no annotations exist, the description carries the burden but only provides minimal information.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences: purpose and return type. It is front-loaded and avoids unnecessary detail, though a brief note on usage context would improve it.

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

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

Given no parameters and an existing output schema, the description is minimally adequate. However, it lacks context about when to use this tool vs siblings and does not hint at the output schema's richness.

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

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

There are no parameters, so schema coverage is 100% trivially. The description adds no parameter semantics, but none are needed. Baseline for 0 parameters is high.

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 'Fetch all available applications from the system,' specifying the verb (fetch), resource (applications), and scope (all). It effectively distinguishes itself from siblings like get_application_info (single app) and get_applications_for_tag (filtered).

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 get_applications_for_tag. The description lacks context about use cases, performance implications, or exclusion criteria.

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 must fully disclose behavior. It describes the return type (List of actions with fields and optional error) but does not mention any side effects, authorization requirements, or potential failures beyond an error message. The description is adequate but lacks depth, such as whether the fetch modifies state or requires specific permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured. It starts with the core purpose, then gives a usage guideline, and finally specifies parameters and return format using clear sections (Args, Returns). No redundant information.

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

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

Given the tool's simplicity (one parameter, well-defined return structure), the description covers all necessary information: what it does, how to use it, what output to expect, and the relationship with 'execute_action'. The output schema is present, so detailed return documentation is not required from description alone.

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

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

The description adds bare minimum parameter info: 'Args: - name (str): Assessment name'. This adds no additional meaning beyond the schema's type and default. Schema description coverage is 0%, so the description should compensate, but it only repeats the parameter name and type without format, source, or constraints.

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

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

The description clearly states the tool's purpose: 'Get actions available on assessment for given assessment name.' It specifies the verb (fetch), resource (assessment actions), and scope (by name). This distinguishes it from sibling tools like 'fetch_evidence_available_actions' and 'fetch_available_control_actions' by explicitly targeting assessment-level actions.

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

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

The description provides clear workflow steps: 'Once fetched, ask user to confirm to execute the action, then use 'execute_action' tool...' This tells the agent when to use this tool (before executing) and how to proceed afterward. However, it does not explicitly state when not to use it or mention alternative tools for different action types.

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

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

The description details return fields and warns about large output ('store it in a file'), but the pagination advice is inconsistent with the schema. With no annotations, it partially discloses behavior but lacks clarity on the missing page parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description front-loads the purpose but then provides an extensive list of return fields. While informative, it is verbose for a tool with an output schema. The structure with Args and Returns is clear.

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

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

The description covers return fields and large output handling, but lacks context on how to obtain the assessment run ID, potential errors beyond the error field, and pagination details. It is adequate but not 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?

The schema has one parameter (id) with 0% coverage. The description adds meaning by labeling it as 'Assessment run id', which clarifies its purpose. However, the reference to a non-existent 'page' parameter detracts from clarity.

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 assessment run details for a given ID, using a specific verb and resource. However, it mentions pagination ('use page to get details pagewise') but the input schema lacks a page parameter, causing slight ambiguity.

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 compared to siblings like fetch_assessment_runs or fetch_run_controls. The description does not specify prerequisites or alternative 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?

No annotations provided. Description discloses return structure and possible error, implying a read operation. Lacks deeper behavioral context like permissions, side effects, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise with clear header and structured args/returns. Every sentence adds value, though the detailed return description could be slightly trimmed if output schema is sufficient.

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, flat output), the description adequately covers purpose, input, and output. It does not mention empty list handling or prerequisites, but is sufficiently complete for a basic fetch 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 coverage is 0%, so description adds value by naming the parameter as 'Assessment run control id'. This adds meaning beyond the bare schema, but could be more specific about format or source.

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 leaf control evidence for given assessment run control id', specifying verb, resource, and input. However, it does not distinguish from siblings like fetch_evidence_records or fetch_automated_controls_of_an_assessment.

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 on when to use this tool versus alternatives, nor any context about prerequisites or conditions. The description only provides input and output details.

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 adds limited behavioral context: it notes large output handling and returns an optional error field. However, it does not disclose permission requirements, side effects (likely read-only), or rate limits. The output schema partially compensates.

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 clarity: it starts with the action, includes a note, then documents args and returns. The return section is detailed but appropriate given the output schema presence. It is not overly 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?

For a simple fetch tool with one parameter, missing annotations, and an output schema, the description is complete. It covers the argument, return structure (matching output schema), and a caveat for large outputs. No significant gaps.

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

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

The only parameter 'id' has 0% schema coverage, but the description explains it as 'Assessment run id,' which adds meaning beyond the bare type string. This is useful for an agent to understand the parameter's purpose.

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 leaf controls for given assessment run id,' specifying the verb (Get) and resource (leaf controls) with the key parameter (assessment run id). It distinguishes from siblings like fetch_leaf_controls_of_an_assessment and fetch_run_controls by targeting a specific run.

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

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

The description provides a usage hint about storing large output in a file, but lacks explicit guidance on when to use this tool versus alternatives like fetch_leaf_controls_of_an_assessment. No exclusions or prerequisites are mentioned.

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 discloses pagination behavior, default page, retry on timeout, and covers possible errors. It also fully lists return fields. However, it does not explicitly state read-only nature or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description contains redundant formatting (extra bullet lists) and some repeated info (e.g., page/pageSize explained twice). Could be trimmed to fewer sentences while retaining 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?

With output schema present and all return fields listed, description covers parameters, errors, pagination, retry logic, and usage context. No critical information missing for an agent to invoke correctly.

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

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

Input schema has 0% description coverage, but description fully explains each parameter: id as assessment id, page and pageSize with defaults and usage. This compensates completely for the schema gap.

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

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

Description clearly states the tool fetches all assessment runs for a given assessment ID, and distinguishes itself from fetch_recent_assessment_runs by specifying when to use it. Verb 'Get' and resource 'assessment runs' are explicit.

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

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

Description provides clear when-to-use guidance: 'use this tool when expected run is got in fetch recent assessment runs tool'. It also gives retry strategy with pageSize. However, it lacks explicit when-not-to-use or alternative tools beyond the one sibling.

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

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

No annotations exist, so the description must carry the burden. It explicitly states the tool returns only basic info and includes an error field. It does not mention authentication or rate limits, but for a fetch operation, the transparency is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a brief purpose, detailed tool purpose, Args section, and Returns section. It is front-loaded and efficient, with no unnecessary words.

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

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

For a simple fetch-list tool, the description fully covers behavior, parameters, and return structure. Even without an output schema, the described return fields (id, name, category_name, error) provide complete 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?

The input schema has 0% description coverage, but the description includes an Args section that explains each parameter (categoryId, categoryName, assessmentName) and their filtering purpose, fully compensating for the schema gap.

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

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

The description clearly states the tool fetches a list of assessments, returning basic info (id, name, category) without full control hierarchy. It distinguishes from sibling tools like list_all_assessments by specifying the limited scope.

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

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

The description provides a specific use case: confirming assessment name while attaching a rule to a control. It implies usage for basic retrieval but does not explicitly mention when not to use or alternative tools.

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

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

With no annotations, the description must convey behavioral traits. It implies a read-only operation but lacks details on authentication, rate limits, side effects, or data freshness. The return field descriptions are incorrect (e.g., 'status' described as 'Name of the asset'), which undermines transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear first line but becomes verbose with a repetitive and incorrect Returns section. The field descriptions are all the same placeholder text 'Name of the asset.', wasting space and potentially confusing the agent.

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 lack of structured output schema, the description attempts to document returns but does so with errors. It omits context like the nature of the summary, pagination, or filtering. The inaccuracies reduce completeness.

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

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

The schema has 0% description coverage, but the description explains the sole parameter 'id' as 'Assessment id', adding minimal semantic value beyond the schema. This is adequate for a simple string parameter.

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 assets summary for given assessment id', specifying the verb and resource. It distinguishes from sibling tools like fetch_checks_summary or fetch_resources_summary by focusing on assets summary.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention preconditions, typical use cases, or when not to use it.

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 must carry the burden. It only lists return fields and an error message, but does not disclose side effects, read-only nature, or the inconsistency between the description (required) and schema (optional). This minimal disclosure is insufficient for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise but includes the return structure, which is redundant given the output schema exists. It could be more streamlined without losing essential 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?

The description provides basic context for parameter and usage, but does not differentiate from similar sibling tools like fetch_controls. The mismatch between required and optional undermines completeness. Given the output schema covers returns, the description is adequate but not thorough.

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

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

With 0% schema description coverage, the description adds meaning by clarifying assessment_id as 'Assessment id or plan id' and noting its requirement. However, it contradicts the input schema which has a default and no required flag, reducing clarity.

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 fetches 'only the automated controls' for a given assessment, using specific verb and resource. It distinguishes from sibling tools like fetch_controls and fetch_leaf_controls_of_an_assessment by emphasizing 'automated'.

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 advises using other tools if assessment_id is not provided, providing clear context for when to use this tool. It implies the ID is necessary, but could more explicitly compare with sibling tools for fetching controls.

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 mentions retrieval and lists output fields. Does not disclose side effects, permissions, or error conditions beyond the error field.

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 somewhat lengthy with step-by-step instructions that could be separate. Front-loaded with purpose but includes redundant agent instructions.

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 return values and provides a workflow but lacks parameter descriptions and has inaccuracies. EvidenceName parameter not addressed.

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?

Description lists three args as required (contradicting schema where only assessmentName is required) and omits evidenceName. No explanation of what each parameter does beyond its name.

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 available actions for a control, matching the tool name. It distinguishes from sibling tools like fetch_controls and execute_action.

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: use fetch_controls if control details missing, prompt user, then use execute_action. However, it does not explicitly state when not to use.

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

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

With no annotations, the description indicates a read-only operation ('Fetch ... details'), but lacks detail on side effects, authentication, or error cases. Adequate for a simple fetch, but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sections (Args, Returns) and uses bold for emphasis. It is well-structured and avoids redundancy.

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

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

Given the simple nature of the tool and presence of an output schema (not shown here), the description sufficiently covers the purpose and parameters. The Returns clause adds 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?

The description explains the only parameter 'rule_id' as 'Rule Id of the rule to retrieve,' adding meaning beyond the schema (0% coverage). Clear and helpful.

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 'Fetch rule details by rule id from the compliancecow,' specifying the verb+resource and distinguishing from siblings like 'fetch_cc_rule_by_name' and 'fetch_cc_rules_list'.

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

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

The description implies usage when you have a rule ID but provides no explicit guidance on when not to use or alternatives. Basic clarity is present, but no comparative advice is given.

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

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

No annotations provided, so description must disclose behavioral traits. It only states it returns a dict, but does not mention it is a read-only operation, error behavior if rule not found, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.