ThinkNEO Control Plane

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable contextual behavior beyond annotations: the audit trail is 'immutable', events are 'cryptographically chained for tamper detection', and it features 'hash verification'. This clarifies the nature and integrity guarantees of the data returned.

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 two sentences, front-loaded with the core action ('Retrieve immutable audit trail'), and every clause provides meaningful context. There is no wasted words or redundancy.

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

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

Given the tool's simple read-only nature, the presence of annotations, and an output schema, the description covers the essential purpose and key differentiator (hash verification). It does not explain the exact output format, but the output schema is present, so that is not required. Slightly more detail on verification semantics could enhance completeness, but it is already 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 description coverage is 100%: both trace_id and workspace have descriptions in the schema. The tool description does not add any extra parameter-specific meaning, so the baseline score of 3 is appropriate since the schema carries the 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 uses a specific verb ('Retrieve') and resource ('immutable audit trail for A2A interactions'), and adds distinguishing detail ('hash verification', 'cryptographically chained') that separates it from sibling tools like thinkneo_audit_export or thinkneo_a2a_log. It clearly states what the tool does.

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

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

The description implies usage for retrieving A2A audit trails with verification, but it does not explicitly state when to use this tool versus alternatives such as thinkneo_audit_export or thinkneo_get_trace. No exclusions or alternative tools are mentioned, so guidance is only implied.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is known. The description adds that data comes from the 'live gateway,' which is a useful real-time context, but doesn't disclose other behaviors (e.g., response format, pagination). With annotation coverage, this is adequate but not rich.

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

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

Two sentences, no redundancy, direct front-loaded verb, and each sentence adds information (what it does and what it shows).

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 visualization tool with a single optional parameter, an output schema, and strong annotations, the description is sufficient. It tells the user what they'll see (agents, approval status, interaction patterns) and that it's live. Could not go to 5 due to lack of sibling differentiation, but overall it's complete in context.

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

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

Schema coverage is 100% for the single 'workspace' parameter, and the description adds no additional parameter details beyond the schema. The baseline of 3 applies because the schema handles parameter documentation.

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

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

Description uses specific verb 'Visualize' with clear resource 'agent-to-agent communication flow' and lists concrete outputs (registered agents, approval status, interaction patterns). It doesn't explicitly name sibling tools for differentiation, but 'visualize' clearly contrasts with 'audit', 'log', and 'policy' siblings.

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

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

No guidance on when to use this tool versus alternatives like thinkneo_a2a_log or thinkneo_a2a_audit. No context about use cases or exclusions; the description only states what it does.

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, idempotentHint, and non-destructive behavior, so the description does not need to restate safety cues. It adds valuable context by mentioning the 'live gateway' and detailing the kind of information returned (costs, outcomes), which goes beyond structured fields.

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

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

The description is extremely concise, consisting of two short sentences that immediately convey the verb, resource, and scope. It contains no filler or redundant information and is easy to parse.

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

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

Given the tool's simplicity, existing annotations, and the presence of an output schema, the description provides sufficient context for an agent to understand what the tool does. It could mention the workspace filtering capability or time bounds, but the schema covers parameters and the output schema handles return details, so the description is largely complete.

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

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

The input schema fully describes both parameters (limit and workspace) with their own descriptions, so the schema carries the semantic load. The tool description does not add any additional parameter-specific guidance, matching the baseline expected for high schema coverage.

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

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

The description uses a specific verb 'Retrieve' and a clear resource 'A2A interaction logs from the live gateway', making the tool's primary function obvious. It further lists what the logs show (which agents called which, actions, costs, outcomes) which adds specificity and helps distinguish it from sibling tools like thinkneo_a2a_audit.

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 states what the tool does but provides no guidance on when to use it versus alternatives such as thinkneo_a2a_audit or thinkneo_a2a_flow. There is no mention of preferred contexts, exclusions, or related tools, leaving the agent without direction for 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?

The annotations already communicate read-only, idempotent, and non-destructive behavior, and the description aligns with these. It adds useful context about the 'live gateway' origin and return content, but does not mention auth requirements or other behavioral nuances. 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 two focused sentences, starting with the action and resource. The second sentence adds valuable detail about return contents without any padding.

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

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

Given the simple tool signature (one optional param, full schema coverage, and an output schema present), the description plus annotations sufficiently cover safety and return scope. No critical information is missing.

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

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

The schema fully documents the single optional 'workspace' parameter (100% coverage), so the baseline is 3. The description does not add extra meaning beyond the schema's 'Workspace identifier' label.

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

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

The description uses a specific verb ('Retrieve') and clearly names the resource ('A2A interaction policies') plus the source ('live gateway'). It also lists the types of information returned, but it does not explicitly differentiate from sibling policy or audit 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?

No explicit when-to-use or alternative guidance is provided. However, the focus on 'allowed actions, rate limits, cost caps, and approval requirements' implies the tool is for retrieving live policy details. There is no sibling comparison or exclusion, so it stops at implied usage.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior, so the baseline is satisfied. The description adds valuable behavioral context by explaining what metrics are computed (value vs. cost, trend, success rate, pre-AI baseline) and that it is an analysis rather than a mutation. 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 two sentences, front-loaded with the primary purpose ('Calculate ROI per AI agent') followed by a concise breakdown of what it shows. Every phrase earns its place, with zero filler or repetition of schema details.

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 moderate complexity (3 optional parameters, no required ones, read-only analytics), the description covers the essential behavioral overview and answers. The output schema exists and annotations are rich, so the description does not need to explain return formats or safety. It could mention how to filter by agent more explicitly, but the schema covers that.

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

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

Schema description coverage is 100%, so parameters are already well-documented (days, workspace, agent_name). The description adds a small amount of context (e.g., 'per agent' and the baseline comparison) but does not meaningfully enrich the parameter semantics beyond what schema provides. 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?

The description starts with a specific verb+resource: 'Calculate ROI per AI agent.' It clearly distinguishes itself from sibling tools by focusing specifically on per-agent ROI, not broader metrics like business impact or savings. It also specifies what it shows (value vs cost, trend, success rate, baseline), leaving no ambiguity about its 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 implies usage by stating the questions it answers: 'Is this agent generating or consuming value?' and 'What's the ROI trend?' This gives clear context for when a user would invoke it. However, it does not explicitly name alternative tools or state when NOT to use it, which would improve the score.

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

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

Annotations already indicate mutation (readOnlyHint=false). The description adds the admin API key requirement and a detailed example showing how parameters interplay, which goes beyond the structured annotations.

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

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

The description is concise and front-loaded with the purpose, followed by a practical example and the auth requirement. Every sentence contributes value without waste.

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

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

Given the tool's complexity (10 params), the example and auth note provide essential context. An output schema exists, so return values are covered. The description is complete for selecting and invoking the tool.

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

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

Schema covers all parameters with descriptions, but the example in the description adds meaningful semantic context (e.g., scope_value and budget_usd for key_spend_pct_of_budget) beyond the schema fields.

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

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

The description clearly states the tool creates a Monitor Agent alert rule with a specific verb and resource. It also provides a concrete example, distinguishing it from sibling tools like delete, list, and test.

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?

Clear context of when to use (creating alert rules) and an explicit prerequisite (admin API key). Does not mention alternatives or exclusions, but the example clarifies usage scenarios.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds the auth requirement (admin API key) and does not contradict any annotations. This is a meaningful addition beyond the structured data.

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 a single 12-word sentence with the verb and object front-loaded. It contains no filler, making it highly concise and well-structured.

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

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

For a simple delete operation with annotations and an output schema, the description covers the essential information: action, resource, identifier, and auth requirement. It could mention irreversibility, but destructiveHint already covers that. The tool is well-specified 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 description coverage is 100%, so both parameters (rule_id and workspace) are fully documented. The description merely repeats 'rule_id' and adds no new semantic details beyond the 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 explicitly states the verb 'Delete', the resource 'Monitor Agent alert rule', and the method 'by its rule_id'. This clearly distinguishes it from sibling tools such as thinkneo_alert_rule_create, thinkneo_alert_rule_list, and thinkneo_alert_rule_test.

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

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

The description implies the use case (deleting an alert rule) and mentions the admin API key requirement, but it does not explicitly define when to use this tool versus alternatives or provide exclusions. The context is clear but relies on inference rather than explicit guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds the admin API key requirement and explains the semantics of what the rules watch, adding context beyond the annotations without contradicting 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 two concise sentences with useful examples (API key budget, spend, error rate) and the notification channels. It is front-loaded with the main action and every sentence adds value with no redundancy.

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

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

This is a simple tool with one optional parameter, a full output schema, and strong annotations. The description adds the domain context (what alert rules do, what they watch, how they notify) and the admin auth requirement, making it complete for an agent to select and invoke confidently.

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% for the single optional workspace parameter, so the schema fully documents it. The description does not add any parameter-specific details beyond the schema, so it meets the baseline but does not exceed it.

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

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

The description states a specific verb and resource: 'List your configurable Monitor Agent alert rules.' It also provides helpful examples of metrics and notification channels. It does not explicitly differentiate from the sibling tool thinkneo_list_alerts, but the resource name 'alert rules' versus 'alerts' provides some implicit distinction.

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

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

The description gives clear context about what the rules monitor and how they notify, and it states a prerequisite: 'Requires an admin API key.' It does not explicitly mention when to use this tool versus alternatives like create/delete/test, but the context implies it is for viewing existing rules.

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 provide no safety hints (all false), so the description carries the burden. It explicitly reveals this is a write action that sends test notifications and requires an admin API key. It doesn't fully disclose potential real-world side effects like live recipients receiving messages, but the term 'test' and 'confirm delivery' make the side effect clear. 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?

Single sentence with the action verb first, followed by object, scope, and a conditional requirement. No filler or redundancy; every element is relevant.

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 test-action with two parameters and an output schema present, the description covers purpose, scope, channels, and auth. It doesn't need to explain return values since an output schema exists. The phrase 'confirm delivery' implies live endpoint usage, which is sufficient context.

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

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

The input schema already provides 100% coverage with clear descriptions for rule_id (sourced from thinkneo_alert_rule_list) and workspace (defaults to API key workspace). The description adds only the channel types (email/discord/whatsapp), which is already implied by 'configured channels.' Thus the description adds minimal value beyond the schema.

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

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

The description opens with 'Send a test notification' – a specific action on a specific resource ('a rule's configured channels'). It clearly states the goal ('confirm delivery') and lists channel types (email/discord/whatsapp), distinguishing it from sibling CRUD tools like thinkneo_alert_rule_create/delete/list.

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

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

The description clearly contextualizes when to use this tool: when you need to verify delivery through a rule's configured channels. It also notes the admin API key requirement, which helps prevent authentication failures. It doesn't explicitly name alternative tools, but the purpose is clear enough to differentiate.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by specifying the data source ('live gateway'), supported formats, and date-range filtering, but does not disclose potential size limits, pagination, or other operational nuances.

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 a single, well-structured sentence with the primary verb and object upfront. Every phrase earns its place: 'live gateway' indicates source, 'JSON and CSV' indicates formats, and 'SIEM integration' indicates use case.

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

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

Given the low tool complexity, complete 100% schema parameter coverage, and presence of an output schema, the description is largely sufficient. It clearly states purpose and context, though a brief mention of output size or time-range limits would make it fully comprehensive.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters (format, start_date, end_date, workspace). The description reinforces the date-range filtering and format choice but does not add significant meaning beyond the schema 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?

Description clearly states the action ('Export audit events'), the resource ('live gateway'), and the scope ('date range filtering for SIEM integration'). This distinguishes it from sibling tools like thinkneo_a2a_audit or thinkneo_get_observability_dashboard, which are not explicitly export-focused.

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

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

The description gives context ('for SIEM integration') that implies when to use the tool, but it does not explicitly state when to prefer this over alternatives or mention any exclusions. Sibling tools suggest related audit/log capabilities, but no direct comparison is provided.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by disclosing an authentication requirement and specifying what the tool shows (quality scores, verification rates, rankings), which goes beyond the metadata. No contradictions with annotations.

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

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

The description is two sentences, front-loaded with the primary action, and includes necessary context (authentication). Every clause contributes meaning without redundancy. It is concise and well-structured.

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

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

Given the presence of an output schema, the description does not need to detail return values. It covers the essential behavioral aspects (comparison, output types, auth) and is sufficient for a moderately simple tool with only two parameters. However, it could briefly mention that providers are optional if left empty, though this is already in the schema.

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

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

Schema description coverage is 100%, with both 'task_type' and 'providers' well-documented in the schema. The description reinforces that task_type is central and that providers are optional, but it does not add new semantic details beyond what the schema already provides. Baseline 3 is appropriate since the schema carries the load.

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 a specific verb ('Compare') and resource ('providers side-by-side') for a specific task type, making the core purpose obvious. It does not explicitly differentiate from sibling tools like thinkneo_compare_models or thinkneo_benchmark_report, though the emphasis on 'real outcomes' and 'verification rates' hints at a distinct benchmark focus.

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

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

The description implies the tool is for comparing providers on a given task type, providing clear context for when it would be used. It does not explicitly name alternatives or state when not to use it, but the purpose is specific enough that an agent can infer appropriate usage. The 'Requires authentication' note adds a practical prerequisite.

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 a safe read-only, idempotent operation. The description adds valuable context by explaining the source of scores (verified outcomes vs. static estimates), what is shown (verification rates, sample counts, rankings), and requiring authentication. This goes beyond the structured fields without contradicting 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 two sentences, front-loaded with the core action ('View the outcome benchmark matrix'), and every sentence adds meaningful detail without redundancy. It is concise and well-structured.

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 optional param, output schema present, full annotations), the description covers the key aspects: purpose, data basis, displayed metrics, and authentication. No critical information is missing for an 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 description coverage is 100%, and the single optional task_type parameter is fully described in the schema with examples and a default. The tool description does not add additional parameter meaning beyond what the schema already provides, so the 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 tool views an outcome benchmark matrix with real quality scores per provider/model/task_type, based on verified outcomes. It distinguishes from siblings like benchmark_compare by emphasizing the matrix view and verified outcomes rather than static estimates.

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

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

The description provides clear context for when to use the tool (viewing verified benchmark scores and rankings) and notes authentication requirements. However, it does not explicitly state when to use alternatives like thinkneo_benchmark_compare or compare_models, so it lacks explicit exclusions.

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 disclose readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description need not repeat those. However, it adds no behavioral context: it does not explain what 'bridging' entails, what happens to the agent, whether a mapping is created, or what the output represents. The description is too vague to be informative 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 extremely short, but under-specification is not the same as conciseness. A single vague phrase 'Bridge A2A agents to MCP tool format' lacks structural elements and does not earn its place as a useful definition; it would benefit from at least one more sentence explaining the action.

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

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

Despite having annotations, an output schema, and a single parameter, the description is fundamentally incomplete. It does not explain what the tool actually does, when to use it, or how it relates to the sibling bridge tools. With such an ambiguous action, the agent cannot reliably select or 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 description coverage is 100% for the single parameter (agent_name: 'A2A agent name to map'), so the schema already provides necessary semantics. The description adds no extra meaning about how the parameter is used, but the baseline of 3 is appropriate given complete schema coverage.

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

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

The description identifies the tool as bridging A2A agents to MCP tool format, giving a specific verb and direction. However, 'bridge' is ambiguous—it could mean convert, map, list, or expose—and it does not distinguish from sibling bridge tools like thinkneo_bridge_generate_agent_card or thinkneo_bridge_mcp_to_a2a.

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

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

No guidance is provided on when to use this tool versus the many alternative bridge tools. There are no context clues, prerequisites, or examples of appropriate scenarios, leaving the agent to guess based solely on the name.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description itself adds no extra behavioral context (e.g., that it generates a card without modifying registry data). It does not contradict the annotations, but it also does not enhance understanding beyond what annotations already communicate.

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 a single, front-loaded sentence that directly states the action. It is concise with no wasted words. However, it is so brief that it borders on under-specification, lacking any additional helpful structure like example use cases or what the output contains.

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

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

For a simple tool with one well-documented parameter and an output schema, the description is minimally viable. However, given the dense sibling context (many bridge/registry tools), the description would benefit from at least one sentence clarifying when to use this specific generator. The current state is sufficient but leaves gaps in understanding the broader context.

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

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

The input schema fully documents the only parameter (agent_id) with its description 'Agent ID from registry'. The tool description's phrase 'from registry data' aligns with this. Since schema coverage is 100%, the description adds marginal value; the 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?

The description clearly states the tool's function: generating an A2A Agent Card from registry data. It specifies a concrete verb (generate) and resource (agent card), making the primary purpose clear. However, it does not explicitly differentiate from sibling tools like thinkneo_bridge_a2a_to_mcp or thinkneo_registry_get, leaving some ambiguity about when this specific tool is preferred.

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 no guidance on when to use this tool versus alternatives such as thinkneo_bridge_a2a_to_mcp or thinkneo_registry_get. It lacks context about typical scenarios, prerequisites, or exclusions. This is a significant gap given the large number of sibling tools in the registry/bridge category.

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, destructiveHint=false, and idempotentHint=true. The description adds the 'all' qualifier and tenant scope but does not disclose any further behavioral traits, such as pagination or filtering behavior. It is consistent 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?

A single, well-structured sentence that immediately conveys the tool's function. No superfluous words or repetition.

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 (no parameters, no nested objects) and the presence of an output schema, the description is fully adequate 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?

The input schema has zero parameters, so schema coverage is effectively 100%. The description's mention of 'for a tenant' suggests the tenant is contextually determined, which is a useful semantic note. Baseline for 0 params is 4.

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

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

The description uses a specific verb ('List') with a clear resource ('MCP <-> A2A bridge mappings') and scope ('for a tenant'). This distinguishes it from sibling bridge tools like thinkneo_bridge_mcp_to_a2a, which convert mappings rather than list them.

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 phrase 'List all MCP <-> A2A bridge mappings' clearly signals when to use this tool (when a tenant's mappings need to be enumerated). However, it does not explicitly mention alternatives or when not to use it, leaving some ambiguity.

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

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

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safe, non-mutating nature is covered. The description adds that it shows tool-to-skill mappings, but does not explain behavior such as whether a missing tool_name means 'all tools' or what the output structure looks like. Since the output schema exists, some return details are offloaded, but behavioral specifics are still thin.

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 consists of two short sentences that are front-loaded with the verb 'Bridge'. There is no filler or redundant information, and every word contributes meaning.

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

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

Given the tool's simplicity (one optional parameter, read-only annotations, and an output schema), the description is mostly adequate. However, it fails to position this tool against the closely related siblings thinkneo_bridge_a2a_to_mcp and thinkneo_bridge_list_mappings, and it does not explain the effect of the optional tool_name parameter. This leaves gaps in understanding when and how to invoke 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?

The input schema covers 100% of the single parameter (tool_name) with a description 'MCP tool name to bridge', so the baseline is 3. The tool description does not add extra meaning beyond the schema, such as clarifying that the parameter is optional or how an empty value is interpreted. It does not compensate for any missing schema detail because none is missing.

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

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

The description uses a specific verb ('bridge') and identifies the resource ('MCP tool registry') and target format ('A2A format'), also stating it shows tool-to-skill mappings. This makes the core purpose clear. However, it does not explicitly distinguish itself from the sibling tool thinkneo_bridge_list_mappings, which likely performs a similar listing function.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like thinkneo_bridge_a2a_to_mcp (reverse direction) and thinkneo_bridge_list_mappings, an agent receives no help selecting among them. The usage context is only implied, not stated with exclusions or alternatives.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds that the tool 'Returns a single view' and lists the metrics, but provides no extra behavioral details such as authentication requirements, rate limits, or data freshness, which falls short of adding significant 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?

The description is two sentences, front-loaded with the tool's role, and every sentence earns its place. The opening phrase 'Executive business impact dashboard' immediately anchors the purpose, and the second sentence adds audience context 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?

The tool is simple with only two optional params and a rich output schema, so the description does not need to explain return values. It accurately conveys the report's scope and metrics, making it complete enough for an agent to choose and 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?

Schema coverage is 100%: both 'period' and 'workspace' have descriptions in the input schema. The description does not add meaning beyond what the schema already provides, so the 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 'Executive business impact dashboard' and 'Returns a single view of' followed by a specific list of metrics, making the tool's purpose unambiguous. It distinguishes itself from sibling tools like thinkneo_agent_roi and thinkneo_decision_cost by positioning itself as the CxO-level aggregate report.

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

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

The description gives clear context: 'This is the report a CxO needs to justify AI investment.' This implies when to use it, but it does not explicitly name alternative tools or state when not to use it, matching the 'clear context, no exclusions' level.

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, idempotentHint=true, and destructiveHint=false. The description adds that the stats come from 'live gateway runtime metrics', which is useful context beyond annotations, but it does not disclose other behavioral traits such as refresh rate, potential latency, or data volume. This is similar to the get_calls example where annotations cover safety and description adds a scoping detail.

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 a single, front-loaded sentence with no fluff. Every word contributes meaning: 'Get' + 'semantic cache stats' + 'live gateway runtime metrics' — exactly what the tool does.

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 zero parameters, a rich output schema, and comprehensive annotations, the description is nearly sufficient. It tells the user what data is returned (cache stats) and from where (live runtime metrics). It could add a note about what kind of stats (e.g., hit rate, size) but the output schema likely covers that, so this is complete enough.

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

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

There are zero parameters, and schema coverage is 100%, so the baseline is 4 per the rubric. The description adds meaning by indicating the stats are 'semantic cache stats' from 'live gateway runtime metrics', but there are no parameters to elaborate on. It appropriately does not waste space explaining nonexistent parameters.

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

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

The description uses a specific verb ('Get'), names the resource ('semantic cache stats'), and specifies the source ('live gateway runtime metrics'). It clearly distinguishes this from sibling tools like thinkneo_get_observability_dashboard, which sounds broader.

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

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

The description implies usage via 'live gateway runtime metrics' but does not explicitly state when to use this tool over alternatives, nor does it provide any exclusion criteria. It gives clear context (real-time cache stats) but no direct guidance on selecting this among the many observability-related siblings.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context: 'Free-tier', 'No authentication required', and specifics about PII types (credit cards, CPF, SSN, email, phone, passwords). 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?

Two sentences, front-loaded with 'Free-tier prompt safety check', and every word adds value. It is appropriately concise with no redundancy.

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

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

For a one-parameter tool with an output schema, the description covers the essentials: what it analyzes, what it returns (assessment with warnings), and the no-auth requirement. No significant gaps remain.

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 clear parameter description. The tool description enhances this by listing the specific PII categories and injection patterns checked, giving the agent more semantic understanding beyond the schema's generic 'text or prompt' 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 uses a specific verb ('analyzes') and resource ('text for prompt injection patterns and PII'), clearly stating the tool's function. It also differentiates itself from siblings like thinkneo_detect_injection and thinkneo_check_pii_international by covering both categories in one free-tier check.

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 indirectly indicates usage context (free-tier, no authentication, combined injection/PII check) but does not explicitly state when to use this tool versus alternatives. Sibling tools like thinkneo_detect_injection or thinkneo_check_pii_international are not mentioned, so the guidance is implied rather than explicit.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds extra behavioral details: 'No authentication required' and 'Luhn validated' for credit cards. These go beyond the annotations and inform the agent about accessibility and validation behavior. No contradiction with annotations found.

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

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

The description is front-loaded with the core purpose and then provides an extensive list of countries and document types. While the country list is lengthy, it is compact and comma-separated, and every piece adds value for international coverage. There is no fluff or repetition.

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 moderate complexity, the description covers the key aspects: what it detects (PII types), where (countries), why (compliance), and accessibility (no authentication). The input schema and output schema (though not shown) handle parameter and return structures. The only minor gap is lack of explicit examples or edge-case handling, but overall it is sufficiently complete.

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

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

Schema description coverage is 100%, so both parameters ('text' and 'countries') are already well-documented. The description lists countries covered, which complements the 'countries' parameter but does not add new syntax or format details beyond what the schema provides. Thus, 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: 'Detect international PII across 30+ document types from 15+ countries.' The verb 'Detect' and specific resource 'international PII' make the purpose unambiguous. The detailed country-specific list (Brazil, USA, UK, etc.) further distinguishes it from generic PII checkers like thinkneo_check.

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

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

The description provides clear usage context: 'Required for LGPD/GDPR/HIPAA compliance.' This tells the agent when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5. The context is clear enough to guide selection among siblings.

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 destructiveHint=false, and the description's 'Check' aligns with these. The description adds useful context by enumerating the governance areas covered (model access, budget limits, data controls, agent governance), which helps set expectations 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 a single, well-structured sentence that front-loads the core action ('Check AI governance policies') and then specifies the scope. Every word adds value, with no redundancy or filler.

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 tool is simple (one optional parameter), annotations cover read-only/idempotent safety, and an output schema exists. The description adequately completes the picture by listing the key policy categories it covers, which is enough for an agent to invoke it correctly.

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

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

The input schema has 100% coverage for the single optional workspace parameter, including a description. The tool description does not add any additional parameter semantics beyond the schema, so the 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 tool checks AI governance policies and lists specific domains (model access, budget limits, data controls, agent governance). It identifies a concrete resource and action, but does not explicitly differentiate it from sibling tools like thinkneo_check_spend or thinkneo_get_compliance_status.

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

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

The description gives no guidance on when to use this tool versus alternatives. It provides context ('from the ThinkNEO gateway') but does not mention exclusions, prerequisites, or when other tools like thinkneo_check_spend or thinkneo_a2a_policy would be more appropriate.

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

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

Annotations already declare the tool as read-only and non-destructive. The description adds behavioral context by specifying that it returns 'real cost breakdown' from the ThinkNEO AI gateway, and by listing the grouping dimensions (provider, model, time period). This goes beyond the structured annotations without contradicting 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 two sentences, concise and front-loaded with the core purpose. It contains no redundant filler and efficiently communicates the tool's function and key features.

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 an output schema and full parameter descriptions, the description is largely complete. It covers scope and return breakdown dimensions. However, it mentions 'team, or project' while the input schema only includes a 'workspace' parameter, creating a minor mismatch. Additionally, no alternatives or exclusions are noted, but this does not significantly impact completeness.

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

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

The input schema provides complete descriptions for all three parameters (period, group_by, workspace), achieving 100% coverage. The description references provider/model/time period, which maps to the group_by and period parameters, but does not add new semantic details beyond the schema. 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?

The description clearly identifies the tool's function as checking AI spend summaries, specifies the scope (workspace, team, or project), and details the output breakdown by provider, model, and time period. This establishes a distinct purpose from similar tools like thinkneo_get_budget_status or thinkneo_get_savings_report.

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

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

The description provides clear context on when to use the tool—when an AI spend summary is needed—and the scoping options (workspace, team, or project). It does not explicitly mention exclusions or alternatives, but the core usage is evident, which aligns with 'clear context, no exclusions'.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds the context of the 'live gateway catalog' as the data source, but does not elaborate on what the comparison entails or how results are structured.

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 a single sentence with no filler. It conveys the core purpose directly and efficiently.

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?

This is a simple tool with one well-documented parameter, an output schema, and annotations covering safety. The description is sufficient for this complexity, though it could benefit from a note distinguishing it from benchmarking or routing tools.

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% because the only parameter 'models' has a full description with an example. The tool description itself adds no additional parameter meaning, so the 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 states the verb 'Compare' and the resource 'available AI models from the live gateway catalog', making the purpose clear. However, it does not distinguish this from sibling tools like thinkneo_benchmark_compare or thinkneo_benchmark_report, which could also involve comparison.

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 no guidance on when to use this tool versus alternatives. Without any mention of exclusions or alternative tools, the agent has to infer the appropriate 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?

The description adds meaningful behavioral details beyond the sparse annotations: it explains that the request is authorized, classified, and policy-checked before any provider is called, and that blocked prompts never reach the model. It also clarifies tenant/workspace derivation from the API key. These are valuable contextual details not present in the annotations, though it stops short of covering error handling or side effects.

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

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

Two sentences, front-loaded with the primary action, and no redundant fluff. Every sentence provides useful information about what the tool does and its governance behavior.

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 an output schema and 100% schema coverage, the description covers the core purpose and a key behavioral guarantee. It omits potential edge-case behaviors like how errors are surfaced or what side effects might occur (e.g., logging, cost), but those are partly mitigated by the available output schema and the clear governance context.

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

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

Schema coverage is 100% with clear descriptions for all five parameters. The description adds no extra semantics about the parameters themselves—it only mentions tenant/workspace derivation, which is not a parameter. The schema already carries the burden, so a 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 a specific verb ('Run'), resource ('LLM completion'), and context ('through the ThinkNEO AI gateway'). It also distinguishes the tool from siblings by emphasizing governance and policy-checking before provider calls, making its unique purpose explicit.

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

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

It provides clear context: this is the tool for governed LLM completions, with the governance flow explained. However, it does not explicitly mention alternatives or offer when-not-to-use guidance. Since sibling tools include policy checks and routing, a note distinguishing those would be helpful, but the intended usage is still fairly 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, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds context about the data source ('live audit data'), which is useful for understanding freshness, but it does not disclose other behavioral aspects like authentication requirements or rate limits. This is acceptable given 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 two sentences, front-loaded with the action and resource, and contains no redundant information. Every word contributes meaning.

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

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

Given the output schema exists, annotations are present, and the input schema is well-documented, the description covers the essential aspects: purpose, frameworks, and data source. It could mention the return format or pagination behavior, but the output schema likely covers that, making the description sufficiently complete.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds value by listing the valid frameworks for the 'framework' parameter, but it does not address 'format' or 'workspace' beyond what the schema provides. 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 a specific action ('Generate a compliance report') and identifies the resource scope (regulatory frameworks: EU AI Act, ISO 42001, SOC2, NIST). It distinguishes itself from sibling tools like thinkneo_get_compliance_status (status vs report) and thinkneo_audit_export (export vs generate) through its focus on generating reports from live audit data.

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

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

The description implies usage when a compliance report is needed for one of the listed frameworks, and notes the data source is live audit data. However, it does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or prerequisites.

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 readOnly and idempotent hints, and the description adds the approximation method 'chars/4 approximation', which is a behavioral disclosure beyond what annotations provide. It does not describe output details or edge cases, but for a simple read-only estimation tool this is reasonably transparent.

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

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

The description is one concise sentence, front-loaded with the core action 'Estimate token count for text' and an efficient parenthetical approximation. No wasted words, and structure follows best practice for short utility tools.

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?

This is a simple tool with one parameter, full schema descriptions, safety annotations, and an output schema present. The description adequately states the tool's purpose and approach, and with the output schema available, no further details about return values are needed. It is complete for its complexity level.

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

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

The schema covers 100% of parameter descriptions (the 'text' field is described as 'Text to estimate tokens for'), so baseline is 3. The tool description adds the approximation algorithm but does not enhance parameter meaning beyond the schema. Thus it meets but does not exceed 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 uses a specific verb 'Estimate' with a clear resource 'token count for text'. It uniquely identifies this tool's function among siblings, none of which share this purpose. The approximation detail further clarifies the exact operation.

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

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

The description implies usage for estimating token counts but provides no explicit guidance on when to use this tool vs alternatives. There are no direct alternatives listed, and no context like 'use before calling a model' is provided. It meets the baseline for implied usage but lacks clear situational direction.

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 idempotentHint=true, and the description adds the specific behavioral detail of comparing to a pre-AI baseline. It does not contradict annotations, though it could disclose dependencies (e.g., requiring a baseline to be set).

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 three concise sentences, front-loaded with the primary purpose. Each sentence adds value: the first states the action, the second explains the output, and the third provides example questions. 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?

The tool has an output schema, so return values are covered. The description adequately explains the core functionality and comparison logic, but could be more complete by mentioning the dependency on a pre-AI baseline or the optional filters. Overall, sufficient for a read-only analytics tool.

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

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

The input schema provides 100% coverage of all four parameters with clear descriptions (period, workspace, agent_name, process_name). The description does not add additional meaning beyond the schema, so the 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 opens with a specific verb ('Analyze') and resource ('cost-per-decision for AI agents'), then clarifies it shows actual AI cost relative to a pre-AI baseline. This clearly differentiates it from other cost-related siblings like thinkneo_check_spend or thinkneo_agent_roi.

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

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

The description implicitly indicates use for cost-per-decision questions, but does not explicitly contrast with sibling tools (e.g., thinkneo_agent_roi, thinkneo_get_savings_report) or state when not to use it. There is no guidance on prerequisites like setting a baseline.

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 idempotentHint=true, so the safety profile is known. The description adds value by disclosing an unexpected side behavior—retrieving live guardrails_blocked stats—and briefly mentions the method ('guardrail patterns'). This goes beyond structured fields, though it omits details about how stats are delivered or whether they affect the detection output.

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 two sentences with no filler. The first sets the primary action, and the second discloses a relevant secondary behavior. Both earn their place, and the structure is front-loaded with the core purpose.

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

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

For a simple one-parameter tool with an output schema and read-only annotations, this description covers the purpose, primary input, and a notable side effect. The only minor gap is the lack of elaboration on how the retrieved stats are presented, but the output schema likely handles that. Overall it is complete enough for an agent to invoke correctly.

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

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

The schema documents the single 'text' parameter with 100% coverage, so the description adds no new semantics beyond restating the parameter's purpose. It does not clarify format, length limits, or encoding, but the baseline of 3 is appropriate when the schema is sufficient.

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 identifies the core action—detecting prompt injection attempts in text—using a specific verb and resource. However, the second sentence introduces a second purpose (retrieving live guardrails_blocked stats), which slightly dilutes focus and could confuse the agent about the tool's primary function. It still distinguishes itself from siblings by naming 'injection' specifically.

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

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

There is no explicit guidance on when to use this tool versus alternatives like thinkneo_evaluate_guardrail or thinkneo_check. No exclusions, prerequisites, or example scenarios are provided, so the agent must infer applicability purely from the name and vague phrasing.

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, idempotentHint=true, and destructiveHint=false. The description adds transparency by detailing the types of findings (e.g., '$3,200/month on error retries') and the analytical scope. This goes beyond what annotations alone convey, 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 front-loaded with the main action and includes useful details and examples. It consists of four sentences, each contributing value, though the final 'buying trigger' sentence is somewhat promotional but still adds usage context.

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

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

Given the presence of an output schema, rich annotations, and a clear description, the tool is well-specified. The description explains the tool's purpose, analysis scope, and output format, while parameter details are left to the schema. It is complete for a read-only diagnostic tool.

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

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

The input schema already provides 100% coverage with descriptions for both parameters (days and workspace). The description does not mention parameters, so it adds no extra meaning beyond the schema, which is the baseline for full coverage.

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

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

The description opens with a specific verb+resource: 'Detect waste and inefficiency in AI operations.' It further specifies what it analyzes (agent performance, A2A communication overhead, error costs, unused capacity, cost outliers) and gives concrete output examples, distinguishing it from siblings like a2a_audit or agent_roi.

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

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

The description provides clear usage context: 'This is the diagnostic tool that creates the buying trigger.' This implies when to use it (to identify waste and drive sales) but does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.

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

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

The description discloses behavioral traits beyond the annotations: it triggers post-session anomaly detection and requires authentication. The annotations are sparse (readOnlyHint=false, etc.), so this additional context is valuable. It does not contradict 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 concise and well-structured: the first sentence states the primary action, the second lists return contents, and the third mentions side effects and authentication. Every sentence adds value with no redundancy.

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

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

For a tool with an output schema and clear annotations, the description covers the essential aspects: purpose, return values, side effects, and authentication. It could mention idempotency or behavior when no trace is active, but these are not critical given the output schema's detail.

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

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

The input schema already provides full descriptions for both parameters (session_id and status), so the description adds minimal extra meaning. The schema coverage is 100%, so the 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?

The description clearly states the tool's function with a specific verb ('End'), the resource ('active agent trace'), and the outcome ('get the session summary'). It also lists what the summary contains, distinguishing it from sibling tools like thinkneo_start_trace and thinkneo_get_trace.

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

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

The description implies when to use the tool: when you want to end an active trace and retrieve its summary. It does not explicitly mention alternatives or exclusions, but the context (ending a trace) is clear and distinct from starting or viewing a trace.

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?

Beyond the read-only and idempotent annotations, the description adds crucial context including return values (risk assessment, violations, recommendations) and a prerequisite (authentication). This enriches the agent's understanding of the tool's behavior 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 concise (two sentences) and front-loaded, stating the primary action and deliverables first. The second sentence adds only one essential detail (authentication). No redundant or irrelevant 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?

Given the presence of a complete output schema, well-documented parameters, and annotations covering safety, the description provides sufficient additional context: its purpose, usage timing, and authentication requirement. It is complete for an agent to decide when and how to invoke the tool.

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

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

The schema descriptions already cover 100% of parameters, including the character limit for 'text' and the mode options for 'guardrail_mode'. The tool description adds no additional parameter-level meaning, so a baseline of 3 is appropriate per the rubric.

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 evaluates a prompt or text against guardrail policies, with a specific purpose ('before sending it to an AI provider'). It also lists the outputs ('risk assessment, violations found, and recommendations'), distinguishing it from generic check tools in the sibling list.

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

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

The description provides a clear usage context: use this tool before sending content to an AI provider. It does not explicitly name alternatives or exclusions, but the context strongly implies when it should be used, which qualifies as clear guidance without explicit 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?

The description adds valuable behavioral context beyond the minimal annotations: it discloses that the score is valid for 30 days, that a public badge URL is generated (indicating a potential side effect), and that authentication is required. This helps the agent understand consequences and prerequisites. However, it does not fully disclose whether existing badges are overwritten or if there are other 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?

The description is composed of six concise sentences, each adding new information: core function, return values, validity, badge URL, framework, and authentication. It is front-loaded with the primary purpose and does not include fluff, though it could be slightly streamlined.

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 (10 dimensions, multiple return components), the description is quite complete: it enumerates inputs, outputs, validity, side effects (badge URL), and prerequisites. An output schema exists, so detailed return formatting is not required. It could mention potential costs or rate limits but is sufficient for selecting and invoking the tool correctly.

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

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

The only parameter 'org_name' is fully described in the input schema (100% coverage) with an example ('Acme Corp'). The tool description does not add additional parameter semantics, but with high schema coverage, the 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?

The description uses the specific verb 'Evaluate' with the resource 'organization AI Trust Score' and lists the 10 dimensions covered. This makes the tool's function immediately clear and distinguishes it from sibling tools like thinkneo_get_trust_badge (which likely retrieves an existing badge) and thinkneo_evaluate_guardrail (which focuses on a single dimension).

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 contextual cues such as 'Score is valid for 30 days' and 'Requires authentication', but does not explicitly state when to use this tool versus alternatives like thinkneo_get_trust_badge. It implies usage for assessing trust score but lacks exclusionary guidance or explicit when-not scenarios.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful context about the included data (spend vs limit, forecast, chargeback) but does not disclose other behavioral traits like aggregation across workspace or potential delays, so it meets baseline 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 a single sentence of 17 words, front-loaded with the verb and resource, and every part adds value. No fluff or redundant phrasing.

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 optional parameter, an output schema, and strong annotations, the description is sufficient. It names the key data categories and source, though it could briefly mention that it covers the overall budget status rather than detailed transactional spend, but the output schema likely fills that gap.

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 optional workspace parameter, which is already described as 'Workspace name or ID'. The description adds no additional parameter detail, so with the schema carrying the burden, a 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 uses a clear verb ('Check') and resource ('AI budget status') and specifies the scope with three data categories (spend vs limit, forecast, chargeback data). It distinguishes this tool from siblings like thinkneo_check_spend and thinkneo_usage by emphasizing budget status and forecast/chargeback specifics.

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

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

The description implies a use case—checking budget status from the ThinkNEO gateway—but provides no explicit when-to-use or when-not-to-use guidance, nor does it name alternative tools for related tasks such as checking current spend versus savings reports. It is clear but lacks exclusions.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds scope (frameworks, governance assessments) but no additional behavioral traits such as auth needs, rate limits, or caching behavior. Given the annotations, this is adequate but not enhanced.

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?

A single, front-loaded sentence conveys the action, resource, and scope without redundancy. Every word earns its place; no filler or repetition.

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 status retrieval tool, the description, annotations, and schema provide sufficient context. The output schema covers return values. Minor gap: 'governance assessments' is vaguely described, but not critical for 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?

The input schema covers 100% of parameters with meaningful descriptions (framework and workspace). The description adds no parameter-specific meaning, so the 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 tool's purpose with a specific verb ('Get') and resource ('compliance status'), and enumerates the supported frameworks (EU AI Act, ISO 42001, NIST AI RMF, SOC 2). This distinguishes it from sibling tools like thinkneo_compliance_generate, which generates rather than retrieves status.

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

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

The context of when to use the tool is clear (retrieving compliance status), but there is no explicit mention of alternatives or when not to use it. For a simple read-only query, this is acceptable, though not as explicit as having a designated alternative.

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 destructiveHint=false, which covers the safety profile. The description adds the notable 'Requires authentication' requirement, which is not captured by annotations. It does not disclose other behavioral traits like rate limits or data freshness, but with annotations covering safety and an output schema, the added value of the auth note justifies a 4.

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

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

The description is front-loaded with the verb and resource, followed by a concise metric list and a helpful analogy. Every sentence contributes value, though the metric enumeration is somewhat long. It remains compact and readable, earning a 4 rather than a 5.

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 optional parameter, rich annotations, and an output schema), the description provides sufficient context: purpose, key metrics, and authentication requirement. It does not explain the effect of the period parameter, but that is already in the schema. The tool is complete enough for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100% (the only parameter 'period' has a full description with allowed values). The tool description adds no extra detail about the parameter, only implying time-based trends. Since the schema already does the heavy lifting, 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 verb 'Get' and the specific resource 'agent observability dashboard', with a detailed list of included metrics (sessions, events, cost, error rate, latency, etc.) that distinguishes it from sibling tools like thinkneo_usage or thinkneo_sla_dashboard. The analogy 'Like Datadog, but for AI agents' reinforces its unique focus.

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

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

The description provides clear context for when this tool is appropriate: when you need aggregated agent observability metrics. It does not explicitly name alternatives or exclusions, but the resource-specific wording ('agent observability dashboard') and metric list make the intended use obvious. Lacks explicit 'use this instead of that' guidance, hence not a 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral context: the proof record is immutable, includes a SHA-256 hash for tamper detection, and requires authentication. This goes beyond the annotations to set expectations about security and integrity.

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?

Two sentences, front-loaded with the action, and every clause adds value: the resource, the included contents, product tie-in, and the authentication note. No filler or redundancy.

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

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

For a simple read-only operation with one parameter, an output schema, and safety annotations, the description is sufficient. It states the prerequisite (verified claim), what the proof record contains, and the authentication requirement, so an agent can correctly invoke the tool.

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

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

The input schema covers 100% of the single parameter (claim_id) with a clear description ('UUID of the claim to get proof for'). The description adds only minor context ('verified claim') but does not change parameter semantics. Baseline 3 is appropriate given full schema coverage.

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

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

The description opens with 'Retrieve the immutable proof record for a verified claim' – a specific verb plus resource. It clearly distinguishes from siblings like thinkneo_verify_claim or thinkneo_register_claim by focusing on retrieval of an already-verified claim's proof, and adds context with 'From Prompt to Proof'.

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

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

The description implies usage after a claim has been verified ('for a verified claim') but does not explicitly state when to use this tool versus alternatives like thinkneo_verify_claim. There is no explicit exclusions or alternative guidance, only contextual implication.

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 mark this as read-only, idempotent, and non-destructive. The description adds the authentication requirement and specifies the report's content (e.g., original cost, savings percentage), providing context beyond the annotations. It does not contradict any 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?

The description is three concise sentences: purpose, output contents, and authentication. It is front-loaded with the primary action and contains no filler or redundancy. Every sentence contributes meaningful information.

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

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

For a simple read-only report with one optional parameter and an existing output schema, the description covers the purpose, the report's contents, and the authentication requirement. It sufficiently equips an agent to decide whether to use this tool and what to expect, without over-explaining schema-provided details.

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

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

The only parameter (period) is fully documented in the schema with a description covering '7d', '30d', and '90d'. Since schema_description_coverage is 100%, the description need not add parameter semantics. The baseline of 3 is appropriate; the description adds no extra 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 uses a specific verb ('Get') and resource ('AI cost savings report'), and enumerates the exact metrics returned (requests routed, original cost, actual cost, savings, percentages, breakdowns). This clearly differentiates it from sibling tools like budget_status or simulate_savings by focusing on historical cost savings data.

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

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

The description implies usage when one needs an AI cost savings report, but it does not explicitly state when to choose this tool over related siblings (e.g., thinkneo_simulate_savings for projections, thinkneo_get_budget_status for budgets). No exclusions or alternative recommendations are provided, so usage context is only implied.

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

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

Annotations already mark it as read-only and idempotent. The description adds the authentication requirement and specifies the detailed return payload (timeline, metadata, cost, duration, alerts), which is valuable beyond the annotations. 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 three concise sentences with no wasted words. Each sentence earns its place: the action, the return contents, and the authentication requirement.

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 a simple one-parameter schema, an existing output schema, and read-only/idempotent annotations, the description covers the key aspects: purpose, return details, and auth. No critical gaps are apparent for this straightforward 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 description coverage is 100% and the session_id parameter is already well-documented with 'Session ID to retrieve the trace for'. The tool description adds little beyond the schema's own description, so 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 verb 'Retrieve' and the resource 'full trace for an agent session', and enumerates the return contents, distinguishing it from related tools like start_trace, end_trace, and observability dashboard.

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

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

The description implies usage for fetching a full session trace but does not explicitly state when to prefer it over alternatives or provide exclusion criteria. It lacks guidance on context such as session lifecycle or comparison with dashboard 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, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: it states that the badge is 'public,' that 'No authentication required,' and that the badge URL is meant for embedding. These details help the agent understand side effects and access requirements. 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 two sentences, front-loaded with the primary action, and contains no redundant filler. Every clause contributes: what the tool does, what it returns, how to use the result, and authentication requirements.

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

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

For a simple one-parameter, read-only getter with a complete output schema and strong annotations, the description is fully sufficient. It covers the authentication requirement, the return content, and the practical embedding use case. No additional detail is needed for successful 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?

The input schema already provides 100% coverage for the single parameter, including a descriptive title and explanation that report_token is a URL-safe string from a trust score evaluation. The description reinforces this but does not add new meaning beyond the schema. Baseline 3 is appropriate since the 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 opens with a specific verb+resource combination: 'Get a public AI Trust Score badge by report token.' It clearly defines the scope (public trust badge), the input (report token), and the output (organization name, score, badge level, validity period). It distinguishes itself from sibling tools like thinkneo_evaluate_trust_score by focusing on badge retrieval versus evaluation.

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

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

The description clearly indicates when to use the tool: to retrieve a public trust badge by report token and embed it via URL. It also clarifies that no authentication is required. It does not explicitly name alternatives or exclusions, so it stops short of a 5, but the context is clear enough for an agent to choose this over 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 the tool as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds that alerts are 'active' and span specific categories, but does not disclose further behavioral traits such as pagination, sorting, or authentication requirements.

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

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

The description is a single sentence that is front-loaded with the action and resource. It contains no redundant words and covers the essential categories without unnecessary detail.

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

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

Given the simple nature of the tool, the output schema is present, and annotations cover safety, the description is fairly complete. It could have mentioned the severity and workspace parameters, but the schema already documents them, and the tool's scope is clearly defined.

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

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

Schema description coverage is 100%, and both parameters (severity and workspace) have clear descriptions. The tool description does not add parameter-level meaning beyond what the schema already provides, so the 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 uses a specific verb ('List') and resource ('active alerts') while specifying the scope across budget, policy, SLA, and security. It clearly distinguishes from sibling tools like thinkneo_alert_rule_list by focusing on current alerts rather than alert rules.

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

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

No explicit guidance is given on when to use this tool versus alternatives. While the name and description imply it is for active alerts, it does not mention exclusions or alternative tools, leaving the agent to infer usage from 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?

The description discloses important behavioral details beyond the annotations, such as the auto-calculation of business value from a baseline if one exists, and provides a concrete ROI example. Annotations are all false hints, but the description adds meaningful context about the tool's behavior without contradicting 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 concise and well-structured, with a clear statement of purpose, a useful clarification about auto-calculation, and a concrete example. Every sentence adds value, and the example effectively illustrates the tool's use.

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 9 parameters, an output schema, and moderate complexity, the description covers the core concept well. It explains the main purpose, the auto-calculation behavior, and gives a practical example. It does not detail every parameter, but the schema fully covers that, so the description is sufficiently complete.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds value by explaining the relationship between process_name and value_generated_usd (auto-calculation from baseline) and by giving a concrete example of ai_cost_usd, value_generated_usd, and ROI. This enriches the schema without repeating it.

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

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

The description uses a specific verb 'Log' and identifies the resource as 'a business decision made by an AI agent,' clearly distinguishing this tool from sibling tools like thinkneo_log_event or thinkneo_log_risk_avoidance. It also highlights the tracking of AI cost and business value, which further clarifies its unique 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 gives clear context for when to use this tool: whenever an AI agent makes a business decision that should be logged with cost and value implications. It provides an illustrative example but does not explicitly state when not to use it or mention alternatives, so it falls just short of a 5.

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

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses that authentication is required and that the call returns event_id and running session cost, implying session state is affected. This is useful context, though it doesn't detail side effects like whether it appends to an immutable log.

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?

Two sentences, front-loaded with purpose, each sentence adds new information (scope, event types, return value, auth). 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?

The description covers purpose, scope, event types, return value, and authentication. It doesn't mention that session_id originates from thinkneo_start_trace, but the schema already specifies that; the output schema exists, so return details needn't be expanded. Adequately complete for a logging 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%—every parameter has a description in the schema. The tool description adds no parametric meaning beyond repeating the event_type list, so baseline 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 opens with a specific verb+resource ('Log an event within an active agent trace') and enumerates six supported event types, making its scope unmistakable. It also states return values, distinguishing it from sibling logging tools like thinkneo_log_decision and thinkneo_a2a_log.

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

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

The description explicitly frames usage as within an active trace and lists the event types it supports, providing clear context. However, it does not mention alternatives or exclusions, e.g., when to use thinkneo_log_decision instead, so it falls short of a 5.

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

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

Annotations provide no safety hints beyond destructiveHint=false, so the description carries the burden. It adds a key behavioral trait: if estimated_impact_usd is not provided, a default is calculated from severity. This goes beyond the schema and is valuable for understanding tool behavior.

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

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

The description is three sentences, front-loaded with the core action. The second sentence provides concrete examples, and the third covers an important behavioral detail. Every sentence earns its place, and there is no fluff or 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?

With an output schema present, return values need not be described. The description covers purpose, examples, and a important default-calculation behavior. It is complete enough for the AI agent to select and invoke the tool correctly, and it fits well among many logging siblings.

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 semantic value by explaining the meaning and default behavior of estimated_impact_usd, and by giving concrete examples for risk_type. This enriches the schema descriptions rather than merely repeating them.

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 specific verb and resource: 'Log a risk event that was blocked or avoided by the governance layer.' It clearly distinguishes this tool from general logging tools like thinkneo_log_event or thinkneo_log_decision by focusing on risk events and avoided impact. The examples further clarify the exact scope.

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

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

The description gives explicit context: use this when logging a risk event that was blocked or avoided, such as PII leaks or prompt injections. It does not explicitly name alternatives or say when not to use it, but the specificity of 'risk event' and the governance layer context provides clear guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds useful context beyond annotations by specifying the object of the check ('connector grants and secrets') and the source ('gateway'), which helps set expectations without contradicting any 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 a single, front-loaded sentence of nine words. Every word contributes meaning, with no filler or redundant detail. It is an model of 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 no parameters and an output schema present, the description is largely sufficient. It tells the agent what the tool does and from where. The only minor gap is that 'secrets status' could be slightly ambiguous, but the output schema likely mitigates this. Overall, 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?

The tool has zero parameters, so the schema covers everything. The rubric gives a baseline of 4 for 0 parameters, and the description does not need to add parameter details. It correctly focuses on the action and scope.

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

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

The description uses a specific verb ('Check') and clearly identifies the resource ('connector grants and secrets status') and origin ('from the gateway'). This distinguishes it from sibling tools like thinkneo_rotate_key (which mutates) and thinkneo_cache_status (which checks a different resource).

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

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

The description implies this tool is used when you need to inspect the status of connector grants and secrets, but it provides no explicit guidance on when to prefer it over alternative tools or any exclusions. With many sibling tools, more explicit context would be helpful, but the basic usage scenario 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, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds a useful behavioral hint that the tool uses 'live metrics context', indicating it may depend on real-time data. However, it does not disclose any additional behaviors like rate limits, authentication, or data handling.

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 a single, compact sentence that front-loads the main action ('Analyze prompt') and adds relevant context ('with live metrics context'). No filler or redundant information is present.

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 simplicity of the tool (one parameter, read-only, no side effects) and the presence of an output schema, the description is sufficiently complete. It conveys what the tool does and a key contextual factor, though it does not elaborate on the nature of the suggestions or any prerequisites.

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

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

The input schema provides a clear description for the only parameter ('Prompt text to analyze'), giving 100% schema coverage. The tool description does not add any additional meaning about the prompt parameter, such as expected format, length limits, or examples, so it relies entirely on 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 uses a specific verb 'analyze' with the resource 'prompt' and clearly states the tool suggests optimizations. This distinguishes it from sibling tools like thinkneo_count_tokens or thinkneo_check, making its purpose immediately obvious.

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 phrase 'with live metrics context' implies the tool is appropriate when live performance metrics are available, but the description does not explicitly state when to use it versus alternatives, nor does it give when-not-to-use guidance. The usage context is only implied, not explicitly defined.

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 convey read-only and idempotent behavior, so the description's burden is lower. It adds 'No authentication required' and 'real-time' information, which are useful operational details beyond what annotations state. No contradictions with annotations are present.

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 three short sentences, each contributing essential information: purpose, output content, and authentication requirement. There is no redundancy or fluff, making it highly concise and well-structured.

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 status tool with two optional parameters, an output schema, and clear annotations, the description is complete. It explains the primary function, what metrics are shown, and that no auth is needed. Nothing critical is missing for an agent to correctly select and invoke this tool.

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

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

The schema provides 100% coverage with detailed descriptions for both parameters (provider and workspace), so the baseline is 3. The description does not add further parameter-specific semantics, but it also does not need to because the schema already defines the allowed values and defaults.

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

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

The description clearly states the tool's purpose: retrieving real-time health and performance status of AI providers via the ThinkNEO gateway. It specifies measurable outputs (latency, error rates, availability), which distinguishes it from sibling tools like thinkneo_cache_status or thinkneo_sla_status that target other resources.

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

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

The description implies when to use the tool—whenever you need provider health or performance status—but it does not explicitly contrast it with alternatives or mention when not to use it. Given the large number of sibling tools, more explicit guidance would help, but the context is clear enough to score above 2.

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

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

With annotations already declaring readOnlyHint, idempotentHint, and destructiveHint=false, the description adds meaningful behavioral detail by specifying the two invocation modes and what each returns (index vs full content). It doesn't contradict annotations and provides additional context beyond 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 concise, two sentences total, with the verb and resource in the first sentence. It is front-loaded with the primary purpose and uses clean, purposeful language without unnecessary detail.

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

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

For a simple read tool with one optional parameter, the description covers all necessary aspects together with rich annotations and a complete schema. It explains both usage modes and the type of content accessible, making it fully adequate for an agent to select and 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?

The input schema already provides 100% coverage with a detailed description of the 'filename' parameter, including examples and default behavior. The tool description reinforces this by explaining the two modes, which adds slight value by framing the parameter's role in the overall operation.

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

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

The description uses a specific verb ('Read') and resource ('Claude Code project memory files'), and clearly explains two modes: returning the MEMORY.md index without arguments or the full content with a filename. It effectively distinguishes from sibling tools like thinkneo_write_memory by focusing on the read operation.

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

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

The description provides clear context on when to use the tool: to access project context, user preferences, feedback, and reference notes. It doesn't explicitly name alternatives or state 'when not to use', but the read/write distinction from siblings is evident and the use case description 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 provide basic safety hints, and the description adds meaningful behavioral context: authentication is required, ThinkNEO will verify the claim, and a claim_id is returned for tracking. It does not introduce any contradiction and supplements the structured data with workflow context.

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

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

The description is three sentences long, front-loaded with the core purpose, and every sentence earns its place. It avoids fluff and does not restate schema details.

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 rich schema and implied output schema, the description covers the essential context: what the tool does, who uses it, the verification loop, authentication, and the return value. It is complete enough for an 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?

The schema has 100% parameter description coverage, so the description does not need to explain parameter syntax. It provides a few illustrative examples of actions, but these largely mirror the schema's own enum values, offering minimal additional semantic value.

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

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

The description clearly states a specific verb ('register') and resource ('action claim'), and explains the agent's role and ThinkNEO's verification follow-up. It distinguishes itself from the sibling 'thinkneo_verify_claim' by focusing on registration and returning a tracking ID.

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

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

The description gives clear context: use this after an agent performs an action, as part of the Outcome Validation Loop. It does not explicitly name alternatives or exclusions, but the intended workflow is easy to infer.

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 read-only and idempotent behavior, so the description doesn't need to repeat those. It adds meaningful context by stating 'No authentication required' and enumerating the specific data fields returned, which goes beyond the annotations and helps set expectations about the output.

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 two concise sentences, front-loaded with the main purpose and followed by the return details. Every piece of information earns its place, and there is no redundancy or filler.

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, read-only, no side effects) and the presence of an output schema, the description is fully adequate. It even enumerates the return contents, which is helpful even though the output schema exists. There are no obvious gaps in context for an agent to properly invoke this tool.

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

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

The schema description covers 100% of the single 'name' parameter with a clear example. The tool description does not add any additional meaning about the parameter, but it doesn't need to because the schema is already sufficient. This aligns with the baseline score for high schema coverage.

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

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

The description uses a specific verb ('Get') and resource ('MCP server package from the ThinkNEO Marketplace'), making the tool's purpose immediately clear. It also distinguishes itself from sibling tools like thinkneo_registry_search and thinkneo_registry_install by focusing on retrieving full package details rather than searching or installing.

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

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

The description clearly implies when to use this tool: when you need full details about a specific package. It lists the rich content returned (readme, tools list, version history, etc.), which helps an agent decide it is the right tool for comprehensive package information. However, it does not explicitly mention when not to use it or point to alternatives like thinkneo_registry_search for lightweight queries.

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

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

The description states 'No authentication required' and 'Tracks the download', disclosing a side effect and access requirement. Annotations are all false (not read-only, destructive, etc.), but the description adds useful behavioral context beyond annotations. It does not fully elaborate on other possible side effects, but for this tool the disclosed information is adequate.

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

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

The description is three concise sentences, front-loaded with the core purpose, and includes only high-value information: return format, client support, side effect, and auth requirement. No waste.

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

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

For a simple 2-parameter tool with an output schema, the description covers the essential context: what it returns, for which clients, the tracking side effect, and auth. No need to detail return values since an output schema exists.

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

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

Schema coverage is 100% with both parameters already described with examples ('thinkneo-control-plane' and client types). The description does not add parameter-level details, but since the schema fully carries that burden, 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 uses a specific verb ('Get') and resource ('installation config for an MCP server from ThinkNEO Marketplace'). It also differentiates from sibling registry tools by explicitly mentioning the ready-to-use JSON config for specific clients (Claude Desktop, Cursor, Windsurf, custom), making its 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 description clearly implies this tool is for retrieving installation configuration, and provides context about the return format and no-auth requirement. It does not explicitly mention alternatives or when-not-to-use, but the context is clear enough for an agent to recognize this is the installation-focused sibling among registry 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?

The description goes beyond the annotations by detailing the exact behavior: calling initialize and tools/list, running a security scan for secrets and injection patterns, computing a 0-100 score, and storing the entry with version history. It also notes that authentication is required. This adds useful context about side effects and validation steps. However, 'Authentication required' is somewhat vague about the mechanism, and the description is slightly redundant due to repeated phrasing.

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 redundant: it repeats nearly the same sentence twice ('Validates the endpoint by calling initialize and tools/list, runs automated security scan...' and 'Validates the endpoint (calls initialize + tools/list), runs security scan...'). The first sentence is sufficiently detailed, and the second sentence adds no new information. This violates conciseness, and the structure is not tight.

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 (10 parameters, mutation, validation, security scanning, version history), the description covers the essential process: validation calls, security scan, score, storage, and authentication. An output schema exists, so return values need not be described. Minimal gaps: it doesn't clarify whether this creates a new entry or updates an existing one, and it lacks specifics about authentication requirements.

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 10 parameters have schema descriptions with 100% coverage, so the schema already provides full parameter semantics. The description does not add any parameter-specific guidance beyond what the schema states. Baseline 3 is appropriate since the schema handles the burden 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 opens with a clear, specific statement: 'Publish an MCP server to the ThinkNEO Marketplace.' This uses a strong verb ('publish') and a concrete resource, making the tool's purpose immediately obvious. It distinguishes itself from sibling registry tools (get, search, install, review) by focusing on the publication action.

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

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

The description clearly establishes the tool's context: it is for publishing a server to the ThinkNEO Marketplace, including validation and security scanning. However, it does not explicitly state when not to use it or mention alternative sibling tools (e.g., 'for installing an existing server, use thinkneo_registry_install'), so the guidance is clear but lacks explicit exclusions.

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?

Beyond the annotations (idempotentHint true, destructiveHint false, readOnlyHint false), the description discloses important behavioral details: reviews update existing entries on repeat, affect the package's average rating in search results, and require authentication. This gives the agent a strong sense of side effects and prerequisites.

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 short and front-loaded with the primary purpose, but the sentence 'One review per user per package (updates on repeat)' is repeated verbatim, wasting a slot. The redundancy is a clear structural flaw despite the otherwise compact wording.

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 low-complexity tool with three well-documented parameters and an output schema, the description covers the essential context: authentication, per-user limitation, update-on-repeat behavior, rating scale, and impact on average ratings. Nothing critical is missing.

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 schema already explains 'name', 'rating', and 'comment'. The description restates the rating range and optional comment but adds no new parameter-specific semantics, so the 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?

The description uses a specific verb ('Rate and review') and resource ('an MCP server in the ThinkNEO Marketplace'), and it clearly specifies a rating scale and optional comment. This makes its purpose distinct from sibling registry tools like search, install, publish, and get.

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

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

The description provides clear context for when to use the tool: rating or reviewing a marketplace package. It also states key constraints—one review per user per package, updates on repeat, and authentication required—but does not explicitly name alternatives or exclusions.

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, idempotentHint, and destructiveHint. The description adds useful context beyond these: 'No authentication required' and a preview of the return fields (name, description, tools count, etc.). This enhances behavioral understanding 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 two sentences long, front-loaded with the core purpose, and contains no filler. Every clause adds value, from the marketplace analogy to the return summary and auth note.

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 a full input schema (5 params, all described), a rich output schema present, and annotations covering safety traits, the description adds essential context: auth requirements and a compact list of returned fields. This is complete for a search tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description's mention of 'by keyword, category, rating, or verified status' mirrors the schema properties (query, category, min_rating, verified_only) but does not add new parameter-level semantics beyond what the schema already provides.

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

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

The description clearly states the tool searches the ThinkNEO MCP Marketplace, lists specific search dimensions (keyword, category, rating, verified status), and describes the return fields. This distinguishes it from sibling tools like registry_install, registry_publish, and registry_get.

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

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

The description implies usage for discovery and search, and mentions the ability to filter by keyword, category, rating, or verified status. It does not explicitly exclude alternatives like registry_get, but the context is clear enough for an agent to decide when to use this search 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?

The annotations already indicate a destructive, non-read-only operation, and the description adds no further behavioral context such as whether the old key is immediately invalidated, whether permissions are required, or what the side effects are. It neither contradicts annotations nor enriches 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 a single, concise sentence that front-loads the core action. It has no fluff, but it is so minimal that it misses opportunities to add helpful context without becoming verbose.

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