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

Annotations only provide readOnlyHint=true. The description adds the URL pattern and states it returns a direct link, which confirms no side effects. It doesn't mention authentication or rate limits, but for a simple link generator this is sufficient.

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

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

The description is well-structured with clear sections and front-loaded purpose. While somewhat verbose, each section adds value. Could be tightened slightly, but is effective and not overly long.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers return value, use cases, workflow, and related tools. It is fully self-contained and leaves no ambiguity 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?

Schema coverage is 100%, so baseline is 3. The description adds context by explaining in the workflow how to obtain the alertId (from list_alerts), but does not provide additional syntax or format details beyond the schema. This is adequate but not exceptional.

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 a clear verb and resource: 'Generate a direct URL/link/weburl for a LogicMonitor (LM) alert.' It explicitly distinguishes from sibling tools by specifying the alert context, and the use cases and workflow further reinforce the purpose.

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

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

The description includes a 'When to use' section listing concrete scenarios like Slack notifications and incident tickets, and a 'Related tools' section that names alternatives. It suggests a workflow starting with list_alerts, but does not explicitly state when not to use this tool or compare directly to sibling generate link tools.

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

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

Annotations include readOnlyHint: true, and description does not contradict. It adds context: returns full URL with group hierarchy, dashboard details, and URL pattern. No destructive behavior mentioned.

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

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

Well-structured with sections, but slightly verbose for a simple tool. Markdown headers and clear sentences. Could be more concise.

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

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

No output schema, so description explains return values: complete URL, group path, dashboard details, and URL pattern. Mentions related tools. Lacks error handling info but sufficient for the task.

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

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

Only one parameter 'dashboardId' with schema description covering 100%. Description does not add significant meaning beyond schema. Baseline 3 is appropriate.

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 generates a direct URL/link for a LogicMonitor dashboard, using specific verb 'generate' and resource 'dashboard link'. It distinguishes from siblings like generate_alert_link and generate_resource_link.

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 'When to use' section with examples (share in Slack, documentation, runbooks, reports) and 'Why use this' explaining value. Also suggests workflow with list_dashboards. Lacks explicit 'when not to use', but context is clear.

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

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

Annotations already declare readOnlyHint=true, meaning no data modification. The description adds value by detailing the return content (URL with group hierarchy, resource details) and explaining that clicking navigates correctly. No contradictions.

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

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

The description is well-structured with bold headers and bullet points, front-loaded with the core purpose. Every sentence adds value without redundancy, achieving 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?

With one parameter, no output schema, and annotations present, the description covers return value format, usage guidelines, workflow, and related tools. It is complete for the tool's simplicity.

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

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

Schema coverage is 100% with a single parameter deviceId described as 'The ID of the resource/device.' The description mentions using deviceId in the workflow but does not add meaning beyond the schema, so baseline score of 3 applies.

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 'Generate' and the resource 'direct URL/link/weburl for a LogicMonitor resource/device.' It distinguishes from sibling tools like generate_alert_link by specifying the target object type.

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?

Explicit 'When to use' bullet points list specific scenarios (incident tickets, alerts, reports). Workflow instructs to first find device using list/search, then use this tool. Related tools list provides 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?

Annotations indicate readOnlyHint=true, and the description confirms this by explaining it creates a shareable URL without modifying anything. It adds context about access requirements and what the link shows.

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 bold headers and sections, but it is verbose with some redundancy (multiple use case examples). Good separation of concerns.

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 low complexity (1 parameter, no output schema), the description is thorough: it explains the return format, workflow, access requirements, best practices, and dependencies (list_websites).

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 100%, baseline 3. The description adds meaning by stating the source of websiteId (from list_websites or search_websites), which provides useful context beyond the schema description.

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

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

The description clearly states the tool generates a direct URL for a LogicMonitor website monitor with full hierarchy path. It distinguishes from sibling tools by specifying the scope (website monitors) and listing related 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 (sharing, documentation, dashboards) and includes common use cases, access requirements, and best practices. It also references related tools.

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

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

Annotations already declare readOnlyHint=true. The description adds value by detailing the return data (resources, users, tenant ID) and providing impact analysis context. It does not contradict annotations; however, it could mention pagination 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?

The description is well-structured with clear sections and front-loaded purpose. While slightly verbose, every section adds value. It is efficient given the complexity.

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

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

Given no output schema, the description thoroughly explains return values and provides workflow and impact context. It is complete for a read-only detail 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 100%, so the schema fully describes parameters. The description provides minimal extra insight (e.g., using list_access_groups to find accessGroupId). Baseline 3 is appropriate as the schema does most of the work.

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

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

The description clearly states 'Get detailed information about a specific access group... by its ID,' which is a specific verb+resource. It distinguishes itself from siblings like list_access_groups and update_access_group by explicitly mentioning them as related tools and providing usage guidance.

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 'When to use' section lists specific scenarios, and the 'Workflow' section advises using list_access_groups first. It also includes 'Impact analysis' for pre-modification context, making it clear when to use this read-only 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?

Annotations already declare readOnlyHint=true, and description adds value by detailing the specific alert attributes returned (severity, threshold, escalation, etc.), though it could explicitly state no side effects (e.g., no state changes).

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

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

Well-structured with sections: purpose, returns, when to use, workflow, related tools. Front-loaded with key details, no redundant sentences, and appropriately sized for the tool's complexity.

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

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

Despite no output schema, the description thoroughly explains return content (alert message, severity, threshold, history, etc.) and places the tool in a clear workflow with related tools, making it 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?

Input schema covers all 3 parameters with descriptions (100% coverage). Description adds a concrete example for the 'fields' parameter ('id,displayName,hostStatus'), aiding understanding beyond the schema's generic description.

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

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

Description clearly states the tool gets detailed info about a specific alert by ID, using a specific verb and resource. It distinguishes from siblings like list_alerts (for finding IDs) and acknowledge_alert (for actions after investigation).

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

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

Explicitly states when to use: after list_alerts to investigate, check thresholds, review history, and before acknowledging. Provides workflow order and links to related tools (acknowledge_alert, add_alert_note, generate_alert_link).

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

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

Annotations already declare readOnlyHint=true, so the description adds value by explaining what details are returned (matching conditions, escalation chain, etc.) and providing context for troubleshooting. No contradictions, but could mention permission needs.

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

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

Well-structured with headings and bullet points. The purpose is stated upfront. It is slightly longer but each section earns its place. Could be tightened, but effective 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 no output schema, the description thoroughly explains what the tool returns and provides rich context about matching conditions and troubleshooting. This fully compensates for the missing output schema and equips the 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 100% with clear parameter descriptions. The description does not add significant new information about the parameters themselves; it focuses on returned fields. Baseline 3 is appropriate.

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 tool clearly states it retrieves detailed information about a specific alert rule by ID, distinguishing itself from sibling tools like list_alert_rules (find rules) and update_alert_rule (modify). The verb 'get' and specific resource 'alert_rule' 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?

Provides explicit 'When to use' bullet points, troubleshooting use cases, and workflow. It directs users to use list_alert_rules to find the ruleId, and mentions related tools for modification and escalation chain. This fully addresses when to use and 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?

Annotations already declare readOnlyHint=true, so the tool is read-only. The description adds detailed behavioral context: returns username, IP, timestamp, description, session ID, affected resources, before/after values. No contradictions.

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

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

The description is well-structured with bullet points for returns, when to use, workflow, and related tools. It is concise, front-loaded with the primary purpose, and every sentence adds value. No wasted 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?

Even without an output schema, the description explains return values in detail. It covers workflow and relationships with sibling tools. For a simple tool with a clear schema, this is complete and sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema; it implicitly references the auditLogId but doesn't elaborate on the fields parameter. No new parameter context provided.

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

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

The description clearly states the tool retrieves detailed info on a specific audit log entry by ID. It specifies the verb 'Get', the resource 'audit log entry', and differentiates from siblings like 'list_audit_logs' and 'search_audit_logs' by explicitly describing its role 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?

Provides explicit when-to-use scenarios (after finding log ID, review exact changes, investigate incidents). Includes a workflow step: first use list_audit_logs, then this tool. Also lists related tools, offering clear guidance on tool selection.

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

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

Annotations already declare readOnlyHint=true. Description adds value by listing specific health indicators (status, numberOfHosts, freeDiskSpace) and their meanings, going beyond the bare annotation.

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

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

Well-structured with sections (Returns, When to use, Health Indicators, Workflow, Related tools). Though lengthy, the structure aids comprehension and all content adds value.

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

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

No output schema; description fully details return fields (description, hostname, platform, etc.) and health interpretations. With only 2 parameters, this is thorough and complete for agent invocation.

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 100%, so baseline is 3. Description adds examples for 'fields' parameter and explains the curated set vs '*', providing extra 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?

Description states 'Get detailed information about a specific collector by its ID' with clear verb+resource. It distinguishes from siblings like 'get_collector_group' and 'list_collectors' by focusing on a single collector's 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?

Explicitly lists when to use (check health, capacity, troubleshoot) and provides workflow ('list_collectors' to find ID, then this tool). Also mentions related tools, giving clear 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?

Annotations already declare readOnlyHint=true, indicating safe read operation. Description adds valuable context about what is returned (name, path, parentId, description, collector counts, subgroups) beyond annotations. No contradictions.

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

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

Description is well-structured with clear sections (Returns, When to use, Workflow, Related tools). Front-loaded with main purpose, concise without 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 read-only tool with one required parameter, the description covers purpose, return contents, usage context, and workflow comprehensively. No output schema, but description details return fields adequately.

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 provides full descriptions for both parameters (groupId and fields), achieving 100% coverage. Description does not add additional meaning beyond what is in the schema, so baseline of 3 is appropriate.

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 detailed information about a specific collector group by ID' – specific verb and resource. Distinguishes from sibling tools like list_collector_groups (find groups) and get_collector (individual collector).

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

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

Explicitly lists use cases (documentation path, membership counts, verify hierarchy) and provides workflow: use list_collector_groups to find groupId first. Also mentions related tools, giving clear guidance on when to use 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?

Annotations already declare readOnlyHint=true. The description adds behavioral context beyond annotations by detailing the return fields (name, displayName, appliesTo, collectMethod, etc.) and explaining key information like lineageId (built-in vs custom). No contradictions.

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

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

The description is well-structured with clear headings (Returns, When to use, Key information, Workflow, Related tools). It is relatively lengthy but each section adds value. Slightly more concise phrasing could improve it, but it is organized and front-loaded with purpose.

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

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

Given the tool has 2 parameters (1 required), no output schema, and readOnlyHint annotation, the description covers all necessary context. It explains return fields, usage scenarios, workflow, and related tools. The agent has sufficient information to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so the input schema already documents both parameters adequately. The description does not add new parameter semantics beyond the schema, but it provides usage context (e.g., how to obtain configSourceId via list_configsources). Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: 'Get detailed information about a specific ConfigSource by its ID in LogicMonitor (LM) monitoring.' It identifies the verb (get), resource (ConfigSource), and scope (by ID). It also distinguishes itself from sibling tools like list_configsources by mentioning its specific purpose.

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 scenarios under 'When to use:', including understanding collected config, reviewing appliesTo logic, checking collection method, and troubleshooting. It also offers a workflow: use list_configsources to find the ID, then this tool. Related tools are listed for 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?

Annotations already declare readOnlyHint=true. Description adds detailed behavioral context: lists returned fields (widgetsConfig, widgetTokens, groupId, sharable) and use cases, confirming no side effects.

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

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

Well-structured with sections and bullet points. Slightly long but each section adds value. Could be more concise but remains clear and front-loaded.

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

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

Given no output schema, description fully explains return content, use cases, and workflow. No gaps for an agent to understand and invoke this tool correctly.

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

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

Schema covers 100% of parameters. Description adds value by explaining how to find dashboardId via list_dashboards and that fields parameter can limit response, but does not elaborate 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?

Clearly states verb 'Get', resource 'dashboard details', and domain 'LogicMonitor monitoring'. Distinguishes from siblings by specifying it's for dashboard retrieval and referencing related tools like list_dashboards.

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 'When to use' bullet points and a workflow (list_dashboards -> get_dashboard -> generate_dashboard_link). Also lists related tools for alternative actions.

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

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

Annotations already provide readOnlyHint=true. Description adds valuable behavioral context: lists specific return fields (name, full path, parentId, etc.) and explains the tool returns 'complete dashboard group details'. This goes beyond annotations by detailing output structure, which is important since no output schema exists.

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

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

Description is well-structured with clear headings (Returns, When to use, Workflow, Related tools). Every sentence serves a purpose; no fluff. Efficient and easy to scan.

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 get-by-ID tool with no output schema, the description is comprehensive. It details the return fields, provides usage scenarios, a workflow, and related tools. It fully prepares the agent to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so both parameters ('groupId' and 'fields') are already described in the input schema. The description does not add new meaning beyond the schema; it only mentions groupId contextually in the workflow. Baseline score of 3 is appropriate.

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 'Get' and the resource 'dashboard group', with specific context ('by its ID in LogicMonitor monitoring'). It distinguishes from siblings like 'list_dashboard_groups' and 'get_dashboard' by focusing on a single group and providing detailed return info.

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?

Explicit 'When to use' section lists concrete scenarios (e.g., 'Get group path for documentation', 'Review permissions before creating dashboards'). Includes workflow ('Use list_dashboard_groups to find groupId') and related tools with clear purposes. No explicit when-not, but context is sufficient.

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

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

Annotations already indicate readOnlyHint=true; description adds detailed return field descriptions and explains appliesTo logic, providing behavioral context beyond annotations without contradiction.

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

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

Well-structured with clear sections (summary, returns, when to use, key info, workflow). Slightly lengthy but every section adds value. Front-loaded with purpose.

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

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

No output schema, but description thoroughly explains returned fields (appliesTo, dataSourceType, datapoints, etc.) and gives examples. Adequate for understanding tool output and usage 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?

Input schema covers both parameters with full descriptions (100% coverage). Description does not add additional parameter semantics; it mentions key returned fields but not parameter usage details.

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

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

Clearly states 'Get detailed information about a specific datasource by its ID' with distinct resource and action. Among sibling get_ tools, this purpose is unique and unambiguous.

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

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

Includes a dedicated 'When to use' section listing specific scenarios (review thresholds, understand appliesTo, etc.). Also provides workflow suggestion to use list_datasources first and relates to sibling tools.

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

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

Description adds context beyond readOnlyHint annotation, detailing return structure and stages. No contradictions. It does not discuss potential limitations like rate limits or auth, but for a read-only lookup, this is 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?

Well-structured with sections, but slightly verbose with repeated stage details. Front-loaded key info. Still concise enough for utility.

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?

No output schema, but description compensates with detailed return info, example, and workflow. Covers essential requirements 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 100%, and description adds meaning: explains fields parameter usage with examples and default behavior. This goes beyond the schema's brief descriptions.

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

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

The description clearly states it gets detailed information about a specific escalation chain by ID. It distinguishes from sibling tools like list_escalation_chains and update_escalation_chain, 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?

Explicitly lists when to use: review notification workflow, verify recipients, check timing, audit methods, troubleshoot. Also provides a workflow: use list_escalation_chains first, then this tool. Related tools are named.

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

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

Annotations already declare readOnlyHint=true, so agent knows it's safe. Description adds detail about what is returned (complete details, specific fields) and key information about response structure. No contradictions.

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

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

Description is well-structured with bold headers, bullet points, and front-loaded summary. Every section (Returns, When to use, Key information, Workflow, Related tools) provides unique value without redundancy.

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

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

Despite no output schema, description fully explains return values (name, displayName, appliesTo, filters, etc.). Provides workflow context and related tools. All necessary information for a get tool is present.

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 100% with both parameters already described. Description does not add new parameter-level detail beyond the schema; it repeats fields but not in a way that increases understanding of parameter usage.

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

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

Description clearly states 'Get detailed information about a specific EventSource by its ID', using a specific verb and resource. It distinguishes from siblings by listing related tools (list_eventsources, list_device_eventsources) and focusing on single item retrieval.

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

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

Explicitly provides 'When to use' section with bullet points covering scenarios like understanding events, reviewing filters, troubleshooting. Workflow guidance is given: use list_eventsources first. Alternatives are named.

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

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

Annotations declare readOnlyHint=true, so the read-only nature is already conveyed. The description adds significant behavioral context: it details return fields, configuration specifics per integration type, and troubleshooting tips. No contradictions. A slight deduction for not mentioning potential side effects or limits, but overall very informative.

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, bullet points, and clear headings. It is front-loaded with the core purpose. However, it is somewhat verbose with repeated troubleshooting advice that could be condensed. Overall, it earns its length by being informative.

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

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

Given no output schema, the description fully compensates by explaining return fields, configuration details, troubleshooting, and workflow. It covers all necessary aspects for an agent to use the tool correctly, including links to sibling tools. Highly complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining what data is returned for various integration types (Slack, PagerDuty, etc.) which helps the agent understand how to use the integrationId parameter effectively. However, it does not explicitly elaborate on the 'fields' parameter beyond what is 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 it gets detailed information about a specific integration by ID in LogicMonitor. It distinguishes from siblings like list_integrations by explicitly stating a workflow: use list_integrations to find the ID, then this tool. The purpose is specific and well-defined.

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 dedicated 'When to use' section listing concrete scenarios (troubleshoot, review config, check API keys, etc.). It also explains the workflow and mentions related tools (list_integrations, test_integration, update_integration) so the agent knows when to use this tool vs 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?

Annotations already declare readOnlyHint=true. The description adds detailed behavioral context by listing what configuration details are returned and troubleshooting use cases, without contradicting 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 headers and bullet points, and the core purpose is front-loaded. While somewhat verbose, every section adds value 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 no output schema, the description thoroughly lists the returned configuration details and troubleshooting use cases. It covers all expected behavior for a read-only retrieval 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 100%, so baseline is 3. The description adds extra context by explaining the netscanId as obtained from list_netscans and implicitly describes the fields parameter through the troubleshooting examples. This raises the score above baseline.

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

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

The description clearly states 'Get detailed information about a specific netscan by ID' with a specific verb and resource. It also distinguishes from siblings by mentioning list_netscans, update_netscan, and run_netscan as related 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 'When to use' section lists specific scenarios like reviewing configuration before running and troubleshooting discovery. It also provides a workflow linking to list_netscans and distinguishes from update/run tools.

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

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

Annotations declare readOnlyHint: true, and the description adds contextual transparency by listing the exact fields returned (note text, timestamp, creator, tags, scope, etc.), with no contradictions.

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

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

The description is well-structured with bold headers and bullet points, concise yet comprehensive, every sentence adds value without redundancy.

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

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

Despite lacking an explicit output schema, the description fully enumerates return fields. It integrates seamlessly with sibling tools and no critical information is missing for a retrieval operation.

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 100%, so the description doesn't provide additional meaning beyond what the schema already offers for both parameters. Baseline 3 is appropriate.

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 detailed info for a specific opsnote by ID, with explicit verb-resource pairing. It distinguishes from siblings by mentioning list_opsnotes for finding notes and create/update opsnote for modifications.

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

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

The description includes a dedicated 'When to use' section with clear use cases and a workflow recommendation to use list_opsnotes first. It also lists related tools for context.

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

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

Annotations already declare readOnlyHint=true, indicating safe read operation. The description adds useful details about returned fields (type, name, contact info, etc.) but does not disclose any additional behavioral traits like permissions or error conditions. It does not contradict 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 sections and bullet points, making it easy to scan. Minor redundancy exists between 'Returns:' and 'Details returned:' sections, but overall it is efficient.

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

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

No output schema is provided, so the description compensates by thoroughly listing return fields (contact info, schedule, rate limit, method). It includes workflow guidance and related tools, making it fully complete 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?

Input schema coverage is 100% since both parameters (recipientId, fields) are described in the schema. The description does not add parameter-level details but explains the return structure, which is acceptable for a baseline score.

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

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

The description explicitly states 'Get detailed information about a specific recipient by ID' with a specific verb and resource. It clearly distinguishes from siblings like 'list_recipients' which finds recipients by ID first.

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 use cases: verify contact info, check schedule, review rate limits, audit configuration. It also suggests a workflow using 'list_recipients' first. Though it lacks explicit 'when not to use' statements, the context is sufficient.

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

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

Annotations already indicate readOnlyHint=true, and the description adds valuable context: what information is returned (members' contact info, escalation chain usage), and the caution that removing a member affects all chains using the group. No contradictions.

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

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

The description is well-structured with sections (Returns, When to use, Key information, Warning, Workflow, Related tools). It is comprehensive but slightly lengthy; however, every section serves a purpose and adds 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?

Despite no output schema, the description exhaustively explains return values (members, usage, description) and provides workflow guidance and warnings. For a tool with two parameters, this is thorough and leaves no ambiguity 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?

Both parameters are described in the schema (100% coverage), and the description adds meaning: groupId is obtained from list, and fields can be a comma-separated list or '*' with an explanation of omitting it. It reinforces the purpose but doesn't introduce new meanings 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 retrieves detailed info about a specific recipient group by ID, and lists what details are returned (members, usage, description). It distinguishes from siblings like 'list_recipient_groups' and 'update_recipient_group' by specifying its role 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?

Explicitly lists four usage scenarios (review membership before modifications, verify notifications, check escalation chain usage, audit notification lists) and suggests a workflow: use 'list_recipient_groups' to find groupId, then this tool before updating. Also mentions related tools and warns about impact of changes.

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

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

Annotations already provide readOnlyHint=true, so the description's behavioral disclosure is not burdened. The description adds considerable context about returned fields (name, type, schedule, recipients, format, data sources, etc.) and configuration details, going beyond annotations. No contradiction.

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

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

The description is well-structured with clear sections (Returns, When to use, Configuration details, Workflow, Related tools) and is front-loaded with purpose. Some redundancy exists between 'Returns' and 'Configuration details', but overall it's efficient and scannable.

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

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

Given no output schema, the description provides detailed return values and configuration details, compensating fully. The workflow and usage scenarios make the tool context-complete for a simple read operation with two parameters.

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 100% (both parameters documented in schema). The description does not add new parameter-level details beyond the schema, but the workflow context indirectly references the reportId. Baseline 3 is appropriate as schema does the heavy lifting.

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

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

The description explicitly states 'Get detailed information about a specific report by its ID' and uses a specific verb (get) and resource (report). It distinguishes from siblings by mentioning related tools and workflow, making the purpose clear and unique.

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 'When to use' section lists concrete scenarios (review configuration, check recipients, verify data sources, troubleshoot, clone settings) and the workflow explicitly suggests using 'list_reports' first. The 'Related tools' section provides alternatives, offering clear guidance on when to use this tool versus others.

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

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

Annotations already indicate readOnlyHint=true, so the description's mention of returning details is consistent. It adds value by specifying the exact return data (name, path, parentId, counts). No side effects or limitations are omitted, but no extra behavioral context beyond the annotations is needed.

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

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

Description is well-structured: a brief summary sentence, bullet list of return values, use-case section, workflow hint, and related tools. Every sentence is informative and earns its place.

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

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

For a simple read tool with full schema coverage and no output schema, the description sufficiently covers return details, use cases, and workflow. No gaps remain for an AI 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 100% coverage with descriptions for both parameters. The description does not add meaning beyond the schema; it only reaffirms the groupId parameter. Baseline score of 3 is appropriate.

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 ('Get'), resource ('detailed information about a specific report group by ID'), and includes the context of LM monitoring. It lists return fields, distinguishing itself from sibling tools like list_report_groups.

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?

A dedicated 'When to use' section lists four specific use cases, and a workflow note suggests using list_report_groups first. Related tools are explicitly named, providing clear guidance on 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?

Annotations already indicate readOnlyHint=true. Description adds behavioral context by listing the types of information returned (displayName, IP, status, etc.) and confirming it's a read operation. No contradictions.

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

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

Well-structured with sections: main purpose, Returns, When to use, Workflow, Related tools. Every sentence is informative and earns its place. No fluff.

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

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

The description fully covers the tool's purpose, return content, usage context, and workflow. Given simple input schema (2 params) and no output schema, it is complete.

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

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

Schema coverage is 100%; description does not add extra detail beyond the schema's parameter descriptions. Baseline score of 3 applies as the schema already documents parameters sufficiently.

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 detailed information about a specific resource/device... by its ID.' Verb and resource are specific, and it distinguishes from sibling tools like list_resources (which lists IDs) and get_resource_datasource (which gets datasource 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?

Provides explicit 'When to use' scenarios, a workflow hint (use list/search first), and related tools. This gives clear guidance on when to use vs 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?

Annotations declare readOnlyHint=true; description adds valuable behavioral context by listing key return fields (instanceNumber, status, alertStatus) and confirming it returns 'complete details'. No contradictions.

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

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

Well-structured with sections: purpose, returns, when to use, key fields, workflow, related tools. Each sentence is valuable; no fluff. Front-loaded with main purpose.

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

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

Despite no output schema, the description compensates by listing return fields and explaining use cases. It covers prerequisites (use list to get ID), and provides enough detail for an agent to decide if this tool is appropriate.

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

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

Schema has 100% coverage, but description adds context: explains 'fields' parameter with examples, and interprets key fields like 'instanceNumber' and 'status' in plain language. This helps agents understand parameter usage and return value semantics.

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

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

The description clearly states the tool retrieves detailed information about a datasource applied to a resource. It uses specific verb 'Get' and resource 'datasource details', and distinguishes from siblings like 'list_resource_datasources' and 'get_datasource' via related tools section.

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?

Explicit 'When to use:' section lists concrete scenarios (check collection, review alert status, etc.). Also provides a workflow suggestion using 'list_device_datasources' first, and lists related tools for alternative actions.

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

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

Annotations already include readOnlyHint=true, but the description adds valuable behavioral context: it explains the returned structure (fullPath, customProperties, etc.) and details about custom properties inheritance. No contradiction with annotations.

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

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

The description is lengthy but well-organized into sections (returns, when to use, key information, etc.). It is front-loaded with the core purpose and each section adds distinct value. Not overly verbose for a complex tool.

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

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

Given no output schema, the description thoroughly explains the return fields (fullPath, customProperties, numOfDirectDevices, etc.) and inheritance behavior. It is complete for a get tool with two parameters.

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 100% (both parameters documented). The description does not add new parameter details but explains the significance of returned fields, which helps interpret the output. The value-add justifies a score above baseline 3.

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 obtains detailed information about a resource/device group by ID, using specific verb and resource. It distinguishes from siblings like list_resource_groups which lists groups, and get_resource which gets a device.

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' scenarios, a workflow referencing list_resource_groups, and related tools. It gives clear context on when to use this tool, such as checking group membership or reviewing inherited properties.

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

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

Annotations indicate readOnlyHint=true; description adds output format, default time range (last 2 hours), max range (1 year), and example. No contradictions.

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

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

Well-structured with clear sections and front-loaded purpose. Slightly verbose but every sentence adds value. Could trim some examples but remains effective.

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?

No output schema, but description details the return format. Covers the prerequisite workflow, time range behavior, and parameter usage. Complete 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?

All 7 parameters are documented in schema. Description adds value by explaining datapoints as comma-separated, start/end in epoch milliseconds with a note to avoid seconds, and a concrete example. Time range tips further clarify behavior.

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

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

Clearly states it gets time-series metrics/datapoints for a resource instance. Distinguishes from sibling list/get tools by focusing on metric values, with examples like CPU utilization and disk usage.

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

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

Explicitly provides usage scenarios (e.g., 'Get CPU utilization for last 24 hours') and a required 3-step workflow involving list_resource_datasources and list_resource_instances. Also mentions time range tips and related tools.

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

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

Annotations already declare readOnlyHint=true. The description adds detail on the permission matrix returned, but no behavioral surprises. Some extra context on return format adds value beyond annotations.

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

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

Well-structured with headings and bullet points, front-loading the main purpose. Slightly lengthy but each section earns its place with examples and use cases.

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

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

Despite no output schema, the description thoroughly explains the return format (permission matrix), covers workflow, and lists use cases. Comprehensive 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 coverage is 100%, so parameters are fully described in schema. The description does not add new information about parameters beyond what the schema provides, meeting the baseline.

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

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

The description clearly states the tool retrieves role details by ID. It uses specific verb 'Get' and resource 'role', and distinguishes from siblings by mentioning related tools like list_roles and create_role.

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

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

Explicitly lists when to use (review permissions, audit, compare) and provides workflow: use list_roles to find roleId first. Also names related tools (list_roles, list_users, create_role) for context.

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

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

Annotations already indicate readOnlyHint, so the safety profile is clear. The description adds value by detailing the return fields (type, times, creator, status) and status semantics. No contradictions. Slight deduction for not mentioning any potential limitations or error conditions.

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

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

The description is well-structured with clear sections (purpose, returns, when to use, status meanings, workflow, related tools). Each sentence adds value, no redundancy, front-loaded with the main purpose.

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

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

Despite lacking an output schema, the description comprehensively lists what is returned (type, device/group, times, duration, comment, creator, status, recurrence). It includes status meanings and workflow context, making the tool fully understandable for agents.

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 100%, so the description does not need to add parameter info. It does not elaborate beyond what the schema provides, maintaining the baseline of 3.

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

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

The description clearly states 'Get detailed information about a specific Scheduled Down Time (SDT) by its ID', with a specific verb and resource. It distinguishes from sibling tools like list_sdts (list all) and create_resource_sdt (create new).

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?

Includes a dedicated 'When to use' section with concrete scenarios (verify creation, check end time, etc.), a workflow (use list_sdts first), and explicit alternative tools. Also explains status meanings to guide interpretation.

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

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

Annotations include readOnlyHint=true, and the description reinforces this by stating it retrieves information. The description adds valuable behavioral context beyond annotations, such as the detailed breakdown of returned data (dependency tree, health calculation, SLA metrics, alert configuration) and a troubleshooting workflow, which fully informs the agent about what to expect.

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

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

The description is well-structured with clear sections and bullet points, making it easy to scan. However, it is somewhat lengthy and contains some redundancy (e.g., 'dependency tree' mentioned in two places). Still, every sentence adds value, earning a 4.

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 (detailed service info) and lack of output schema, the description fully covers what is returned (dependency tree, health, SLA, alert config), usage context, and a workflow. No gaps remain for an agent to understand its purpose and invocation.

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 100%, so the baseline is 3. The description adds extra value by providing examples for the 'fields' parameter (e.g., "id,displayName,hostStatus") and noting the default behavior when omitted, which enhances understanding beyond 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 the verb ('Get'), resource ('detailed information about a specific service'), and scope ('by ID' in LogicMonitor monitoring). It distinguishes itself from sibling tools like list_services (which lists services) by specifying retrieval of a single service's 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?

Explicit 'When to use' section outlines five specific use cases (review dependencies, check health, verify SLA, troubleshoot, understand architecture). Provides a workflow: use list_services first, then this tool. Lists related tools (list_services, update_service, list_resources) for alternative actions.

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

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

Annotations already declare readOnlyHint=true, so the agent knows it's a read operation. The description adds detail about the return content but does not disclose additional behavioral traits like authentication requirements, rate limits, or side effects. The description is consistent with annotations and does not contradict them.

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

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

The description is well-structured with clear sections (Returns, When to use, Workflow, Related tools) using markdown headers and bullet points. It is readable but slightly verbose; some redundancy exists (e.g., the first sentence is repeated in Returns). Overall, it effectively communicates without being excessively long.

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

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

Given no output schema, the description compensates by listing the return fields (name, full path, etc.). It also provides usage workflow and related tools, making it complete for a simple get tool with 2 parameters. It could mention handling of the optional fields parameter or limits of the response, but overall it provides sufficient context for an 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?

Schema coverage is 100% with both parameters (groupId, fields) described. The description does not add new semantic meaning beyond what the schema provides for these parameters. However, the listing of returned fields gives context that might indirectly help parameter selection but does not directly enhance parameter semantics.

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

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

The description clearly states the tool retrieves detailed information about a specific service group by ID. It specifically uses the verb 'Get' and resource 'service group', and the returns section lists specific fields. It differentiates from sibling tools like list_service_groups (which finds groups) and get_service (for individual services), as shown in the related tools section.

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' bullet points (e.g., get group path, check membership counts) and a workflow that instructs to use list_service_groups first to find the groupId. It also lists related tools with their purposes, giving clear guidance on 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?

Annotations already provide readOnlyHint=true, but the description adds significant behavioral context: what is returned (relationships, layers), how discovery works (SNMP, CDP, LLDP), and the nature of the data (automatically discovered maps). It does not discuss rate limits or performance, but for a read-only query tool, this is sufficient.

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

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

The description is well-structured with headings and bullet points, making it easy to scan. However, it is verbose with repeated phrases like 'resource/device' and covers extensive explanatory material that could be condensed. Still, front-loading the purpose and returns helps agents quickly grasp the tool.

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 no output schema and moderate complexity, the description is remarkably complete. It explains what topology is, when to use it, what information is included (layers, connections), use cases, and even how discovery works. This enables an agent to understand the tool's domain and invocation context thoroughly.

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 100% for the single parameter 'fields,' which is adequately described in the schema. The description does not add any additional semantic meaning or examples for this parameter, so it meets the baseline but does not exceed.

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 network topology information in LogicMonitor (LM) monitoring.' It specifies the exact resource (network topology) and distinguishes from sibling tools like list_resources and get_resource via the 'Related tools' section, making the purpose unambiguous.

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

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

The description includes a dedicated 'When to use' section listing explicit use cases (e.g., understand network architecture, troubleshoot connectivity). It also names alternatives in 'Related tools,' guiding the agent on when to choose this tool over others. No explicit exclusions, but the guidance is comprehensive.

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?

Describes return fields in detail (username, email, roles, status, etc.) and explains their significance (e.g., 'status: active vs suspended'). Includes security audit use cases. Annotations already indicate read-only, and description reinforces safety with behavioral context.

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

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

Well-structured with sections and bullet points. Every sentence adds value, though somewhat lengthy. Front-loaded with purpose. Could be slightly more concise, but 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?

Despite no output schema, description provides thorough coverage of return fields, use cases, and workflow. Complements high schema coverage and readOnlyHint annotation. Complete enough for an agent to understand what the tool does and how to use it.

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 covers both parameters with descriptions. Description adds no new parameter-level detail beyond schema. Baseline 3 for high schema coverage (100%). Description compensates slightly by explaining return fields that relate to the 'fields' parameter, but not enough to exceed baseline.

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

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

Clearly states 'Get detailed information about a specific user by their ID in LogicMonitor (LM) monitoring.' Differentiates from sibling tools like list_users (find user ID) and update_user (modify). Purpose is specific and unambiguous.

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

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

Explicitly lists when to use: review permissions, check last login, verify contact, audit before modification, API token management. Provides workflow: use list_users to find userId then this tool. Mentions related tools for 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?

Description reinforces the readOnlyHint from annotations by stating it returns data. It details what configuration is returned (steps, checkpoints, thresholds, SSL) and includes use cases for troubleshooting, adding significant context beyond annotations.

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

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

Well-structured with sections and bullet points, but slightly verbose with multiple use case lists. The core purpose is front-loaded, and each section adds value, though some redundancy exists.

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?

Thoroughly explains return values (name, type, checkpoints, thresholds, SSL, etc.) and no output schema is present. The description covers all important aspects for a read tool, including related tools and a workflow.

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 100%, so baseline is 3. The description does not add significant new parameter-level detail beyond what the schema already provides for 'websiteId' and 'fields'.

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

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

The description clearly states the verb ('Get'), resource ('website monitor'), and scope ('by its ID in LogicMonitor'). It distinguishes from sibling tools like 'list_websites' and 'update_website' by specifying unique functionality.

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

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

The description includes explicit 'When to use' and 'Workflow' sections, listing specific scenarios (e.g., review config, troubleshoot) and suggesting using 'list_websites' first. It also lists related tools with descriptions, providing clear guidance on 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?

Annotations already declare readOnlyHint=true, and the description is consistent, describing a read operation. The description adds details on what the response contains but could explicitly state no side effects.

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

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

Structured with clear sections (Returns, When to use, Workflow, Related tools). Every sentence is informative and no redundant content.

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

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

Covers purpose, usage context, return values, and workflow. For a read-only get tool with good annotations, this is fully complete.

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

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

Schema coverage is 100%, so the description does not need to explain parameters deeply. It only mentions 'by its ID' for groupId, which adds little 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 'Get detailed information about a specific website group by its ID' and lists what is returned. It distinguishes from sibling get tools by specifying the resource type (website group).

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 a dedicated 'When to use' section with specific use cases and a 'Workflow' section recommending prior use of list_website_groups. Related tools are explicitly named, offering 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?

Annotations already provide 'readOnlyHint: true', but the description adds valuable behavioral context: it notes that a negative 'total' value indicates incomplete results and explains pagination behavior (use size/offset or autoPaginate). This goes beyond the annotations and helps the agent understand edge cases.

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 (returns, what are, when to use, etc.) but is verbose. It could be more concise, as multiple paragraphs elaborate on concepts that could be summarized. The length is acceptable but not optimal for quick scanning.

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 5 parameters, no output schema, and no nested objects, the description provides a thorough explanation: it states the purpose, usage guidelines, return fields, conceptual background, behavioral notes, and related tools. The explanation of what access groups are and the examples cover the context needed for correct 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?

Schema description coverage is 100% (all 5 parameters are described in the schema). The description does not add per-parameter details beyond the schema. However, it does include an 'Important' note about pagination and negative total, which indirectly relates to the pagination parameters. The baseline is 3, and this minor addition does not elevate the score significantly.

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: 'List all access groups in LogicMonitor (LM) monitoring.' It uses a specific verb ('list') and resource ('access groups'). It distinguishes from sibling tools like 'get_access_group' by noting it returns a list, and it explicitly contrasts with roles. The purpose is unambiguous and well-explained.

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

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

The description includes a dedicated 'When to use' section listing specific scenarios (multi-tenant, departmental isolation, environment separation). It also provides a crucial distinction between access groups (what you can see) and roles (what you can do), and gives a workflow: use list to find groups, then assign users via 'update_user'. This explicitly guides the agent on when to use this tool vs 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?

Annotations already set readOnlyHint=true, but the description adds valuable behavioral details: explaining that a negative 'total' indicates incomplete results, that pagination with size/offset or autoPaginate should be used, and describing how rules are evaluated in priority order. This goes well beyond the 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 long but well-organized with sections, bullet points, and examples. Every section adds value, including common patterns and troubleshooting. It front-loads the core purpose. A slight reduction in length could improve conciseness, but the structure is excellent.

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

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

The description covers the tool's purpose, usage scenarios, behavioral notes, parameter hints, response structure (array of alert rules with fields), and troubleshooting tips. It is comprehensive despite lacking an output schema, and addresses potential pitfalls like negative totals and pagination.

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 100% coverage with clear descriptions for each parameter. The description adds extra value by explaining the filter syntax with specific examples and the significance of autoPaginate for large result sets. It does not repeat schema content but supplements it 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 'List all alert rules in LogicMonitor (LM) monitoring' and explains what alert rules are, distinguishing from siblings like get_alert_rule and list_escalation_chains. The verb 'list' and resource 'alert rules' are specific, and the additional context about routing logic and priority order makes the purpose unmistakable.

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

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

The 'When to use' section provides explicit scenarios such as auditing notifications, understanding routing, troubleshooting, and documentation. It also lists related tools. However, it does not explicitly state when not to use this tool (e.g., for detailed rule inspection vs. list overview), but the context is clear enough for an agent.

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

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

Annotations indicate readOnlyHint=true, and the description adds behavioral details such as autoPaginate behavior, negative total meaning, and the conversion of query to filter. No contradiction with annotations.

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

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

The description is well-structured with sections (Returns, When to use, Two search modes, Important notes) and front-loaded with purpose. While slightly verbose, every sentence adds value.

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

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

Given no output schema, the description provides a comprehensive overview of return fields, pagination, modes, and limitations. It fully equips the 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?

All 7 parameters have schema descriptions (100% coverage), and the description adds extra context: distinction between query and filter, conversion rules, and common filter patterns. This goes beyond the schema.

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

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

The description clearly states the tool lists active alerts in LogicMonitor and specifies the return structure. It distinguishes from sibling tools like get_alert, acknowledge_alert, etc., by providing context on related functionalities.

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 'When to use' section lists concrete scenarios (critical alerts, unacknowledged, etc.) and explains two search modes with guidance on when to use each. It includes important notes about OR operator limitations and negative total handling.

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 annotations say readOnlyHint=true, and the description adds important behavioral details: negative 'total' indicates incomplete results, pagination behavior, and security considerations about token permissions, going beyond annotations.

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

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

The description is well-organized with sections, but is quite long and includes some redundant explanation (e.g., what are API tokens) that an AI might not need. Still, it is structured and front-loaded with key info.

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 (6 params, no output schema), the description is thoroughly complete: it covers return fields, use cases, best practices, workflows, and edge cases like negative total. No output schema needed due to detailed description.

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

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

Schema coverage is 100% so baseline is 3; the description adds context (e.g., userId from list_users) and an important note about autoPaginate, but does not significantly enhance parameter meaning beyond the schema.

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

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

The description clearly states the tool lists API tokens for a specific user, and includes a workflow linking to sibling tools like 'create_api_token' and 'delete_api_token', distinguishing it well.

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 dedicated 'When to use' section with specific scenarios (audit, find stale tokens, inventory), a 'Security workflow', and mentions related tools, giving clear guidance on when and how to use.

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