mcp-interaction-studio

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation3/5
Tools are mostly distinct but several overlap: list_segments vs list_all_segments, get_segment vs get_segment_details, and audit_dataset vs audit_mcp_dataset could confuse an agent. Descriptions help differentiate but ambiguity remains.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with lowercase and underscores (e.g., list_recipes, set_campaign_state). The verbs vary appropriately but the pattern is uniform across all 22 tools.
Tool Count4/5
22 tools is on the higher side of reasonable for a complex domain like Interaction Studio, covering datasets, campaigns, segments, recipes, and audits. It is slightly above the typical well-scoped range but not excessive.
Completeness2/5
The tool set heavily favors read operations and state toggles but lacks create, update, or delete tools for campaigns, segments, recipes, or surveys. This is a significant gap for full lifecycle management.
Average 3.5/5 across 22 of 22 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It only states what the tool does but fails to mention that it is a read-only operation, does not modify state, or any other side effects or constraints. Since it is a listing operation, the behavior is partly implied, but full transparency is lacking.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately conveys the tool's purpose. It is front-loaded and contains no unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, so the description should hint at the response structure (e.g., list of segments with count and status). It does not mention what the return format looks like or that the result can be filtered by segment_ids. Given the tool's complexity, more context about the output is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides clear descriptions for all 4 parameters (100% coverage), including valid values for time_range. The description adds no additional semantic information beyond what the schema provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists membership counts and calculation status for segments. It uses a specific verb ('list') and resource ('segment stats'), and distinguishes from sibling tools like list_segments and get_segment_details by including 'stats'. However, it could be more explicit about how it differs from similar list tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 such as list_segments or get_segment_details. It does not specify prerequisites or which parameters are necessary for common use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only says 'List,' implying a read operation. It discloses no behavioral traits such as required permissions, rate limits, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 13 words, directly stating the purpose. No redundancy, front-loaded with the action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema, the description covers the basic purpose but lacks details about output format, pagination, or what constitutes activity logs. It is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with adequate parameter descriptions. The description adds minimal meaning beyond the schema, framing the parameters vaguely ('for an Interaction Studio dataset within a time range').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (List), resource (content zone activity logs), and scope (for an Interaction Studio dataset within a time range). It is distinct from sibling tools like list_recipes or get_campaign_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or provide context about when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It only states the action without revealing side effects, authentication needs, rate limits, or behavior when the optional dataset parameter is omitted. For a read operation, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 8 words, with no filler or redundant information. It is front-loaded and communicates the essential purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimally adequate for a simple listing tool with one optional parameter and no output schema. However, it lacks details on return format, default behavior when dataset is omitted, and potential pagination, which would be useful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, meaning the schema already describes the dataset parameter. The description adds no further meaning beyond what is in the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('list') and resource ('surveys') within the context of 'Interaction Studio dataset'. It effectively distinguishes from sibling tools like list_campaigns and list_recipes, though the exact scope (all surveys or per dataset) is slightly ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or when it would be appropriate to choose list_surveys over other list tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears the burden of behavioral disclosure. It does not state read-only nature, data sensitivity, permission requirements, error conditions (e.g., missing datasets), or rate limits. The 'side-by-side' phrase hints at read-only but is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is efficient and front-loaded with the core action. It covers multiple aspects without excessive length. However, it could be slightly more concise by omitting 'side-by-side' which is implied.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters and no output schema, the description describes the purpose and scope of comparison adequately but omits return format, time range implications, and assumptions about first dataset defaulting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds context by listing compared aspects, but does not enhance parameter understanding beyond schema details (e.g., no format, constraints, or default values).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb 'Compare' with a clear resource 'two Interaction Studio datasets' and lists the specific aspects compared (counts, campaign states, segment populations, recipe linkage, and activity). This clearly differentiates it from sibling tools that handle individual datasets or audit functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like audit_dataset or get_dataset. There is no mention of prerequisites, exclusion criteria, or when comparison is beneficial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description fails to disclose behavioral traits such as output format, pagination, ordering, permissions, or side effects. The description is minimal, leaving the agent without critical usage context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, directly states purpose with no extraneous words. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacking annotations and output schema, the description does not explain return values, potential errors, or scope limitations. For a simple list operation it is barely adequate but incomplete for safe autonomous use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'dataset', and its schema description is adequate. The tool description adds no additional meaning beyond the schema, but baseline is 3 due to high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'recommendation recipes' with context 'in an Interaction Studio dataset'. This distinguishes it from sibling tools like list_surveys or list_campaigns, providing specific purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 (e.g., get_recipe_usage). Does not mention prerequisites or the optional dataset parameter's condition (IS_DEFAULT_DATASET). Agent receives no context for invocation decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Lacks any behavioral details beyond listing. No mention of authentication, rate limits, or side effects. With no annotations, description carries full burden but falls short.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single concise sentence with no extraneous content. Every word contributes to the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple list tool, but lacks return format or pagination details. No output schema to compensate, so some incompleteness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and description adds value by noting optionality based on IS_DEFAULT_DATASET configuration, which goes beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all segments in a dataset and includes id and name. It is specific and distinguishable from siblings like 'list_all_segments', though not explicitly differentiating.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_segment' or 'list_all_segments'. Missing when-not and context prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states the purpose but does not disclose behavioral traits like read-only nature, required permissions, or any side effects. The term 'runtime context' is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, clear sentence that is front-loaded with the action and resource. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no output schema, and no annotations, the description is somewhat thin. It does not explain the return structure or any additional context needed for effective use. Moderate completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and all parameters have descriptions. The description adds high-level context about what the output contains but does not add meaning 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (Get), the resource (runtime context for an Interaction Studio campaign), and lists what is included (experiences, messages, content configuration). It distinguishes from siblings like get_campaign and get_campaign_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 such as get_campaign or get_campaign_stats. The description does not provide any when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only lists metrics but does not disclose auth requirements, rate limits, side effects, or return format. For a read operation, minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no waste. Lists specific metrics efficiently. Could be slightly more structured (e.g., mentioning time_range), but overall concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but description does not hint at return structure or aggregation. The description omits the time_range context that is part of the schema. For a stats tool, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all three parameters. The description adds no additional meaning beyond the schema, resulting in baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get performance stats for an Interaction Studio campaign' and lists concrete metrics (impressions, clicks, goals, orders, revenue). This distinguishes it from siblings like get_campaign (campaign config) and list_campaigns (listing campaigns).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied (when you need campaign stats), but no explicit guidance on when to use this vs alternatives like get_campaign or list_campaign_stats (if such existed). No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It only states 'Get... metadata', implying a read-only operation, but does not explicitly confirm safety or describe side effects, authorization needs, or rate limits. The description adds no behavioral context beyond what the tool name suggests.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence of 14 words that directly states the purpose and what is included. Every word contributes value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists return fields but does not specify response structure (single object vs. array), error conditions, or behavior when dataset is not found. It is adequate for a simple get operation but lacks important contextual details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 lists fields in the response but does not add meaning to the parameters themselves (dataset name, include_content_zones) beyond what the schema already provides. Thus, it neither improves nor degrades parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('Interaction Studio dataset metadata'), listing specific fields like label, state, tracking settings, and content zones. It distinguishes this tool from siblings like list_datasets (which lists all datasets) by implying a single-dataset focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 vs alternatives such as list_datasets or audit_dataset. The usage context is merely implied from the tool name and description, with no explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the full burden. It does not explicitly state that the tool is read-only, non-destructive, requires no specific permissions, or any other behavioral traits. The name 'get' implies read, but no confirmation or additional behaviors (e.g., rate limits, response size) are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no fluff, front-loaded with key action and scope. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and no annotations, the description provides the essential purpose but lacks details on return format, common use cases, or differentiation from siblings like get_segment_details. It is minimally complete but not fully informative for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds one relevant phrase ('including optional targeting rules') that corresponds to the include_rules parameter, but does not elaborate on the other three parameters (dataset, segment_id, include_raw) beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get details for a single Interaction Studio segment by id, including optional targeting rules.' It specifies the verb (get), the resource (segment), the scope (single, by id), and an optional component (targeting rules). This distinguishes it from sibling list tools like list_segments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., list_segments, get_segment_details). It omits prerequisites, context for parameters like include_raw, and when not to use. Only the name and vague mention of 'optional' provide indirect hints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description mentions output options and file write but fails to state whether the tool is read-only, requires special permissions, or has side effects. Incomplete disclosure for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with clear enumeration of audit scope and output options. Efficient but could be slightly more structured (e.g., separate usage notes).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 5 optional parameters and no output schema, description provides a high-level list of audit contents but lacks specifics on return format structure or error conditions. Adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have schema descriptions (100% coverage), so baseline is 3. Description adds little beyond schema—e.g., notes optionality and defaults already present in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Run a complete Interaction Studio dataset audit' and lists specific components (configuration, campaigns, segments, etc.), distinguishing it from sibling tools like list_recipes or get_campaign.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied as a comprehensive audit tool, but no explicit guidance on when to use versus alternatives (e.g., individual list/get tools) or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states the operation and return fields, lacking details on error handling, authentication needs, rate limits, or whether results are paginated. This is minimal for a list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence states the purpose and the second lists the return fields. Every word adds value, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is somewhat complete for a simple list tool, specifying the returned fields. However, it lacks details on pagination, result limits, or default dataset behavior, which are relevant given the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (both parameters have descriptions in the schema). The description adds no additional meaning to the parameters beyond what is already in the schema, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List campaigns') and the resource ('in an Interaction Studio dataset'), and specifies the returned fields (id, name, state, priority, last modified time). This distinguishes it from sibling tools like list_recipes or list_segments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (when you need to list campaigns in a dataset) but does not explicitly state when not to use it or mention alternatives like get_campaign for a single campaign. No guidance on prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only states it lists templates. It does not disclose behavioral traits such as pagination, sorting, filtering, authentication requirements, or any side effects. For a list operation, more transparency is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, no waste. The description is front-loaded with the verb and resource, making it immediately clear. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description is minimally adequate. However, it doesn't specify what the list returns (e.g., template IDs, names, structure), which would be helpful given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (1 parameter with description). The tool description adds context that the templates are 'item/message' and 'used for campaign content', going beyond the schema's 'Dataset name' explanation. This enriches the meaning for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list', the resource 'item/message templates', and the context 'in an Interaction Studio dataset used for campaign content'. It distinguishes from sibling tools like list_recipes and list_campaigns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools (e.g., list_recipes, list_campaigns), providing when/to use or when-not-to-use would help an agent select correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions optional live stats and membership counts but does not detail side effects, authentication needs, or rate limits. The read-only nature is assumed but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence of 12 words. Every word contributes to the purpose. No wasted text or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite good schema coverage, the description lacks details on return format, error handling, and what 'extended' includes beyond membership counts. For a tool with 5 parameters and no output schema, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all parameters are described in the input schema. The description adds minimal semantic value beyond 'optional live stats', which corresponds to the already-documented include_stats parameter. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves extended segment details with membership counts and optional live stats. It distinguishes itself from sibling tools like list_segments and get_segment by specifying 'extended' and specific content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when extended details are needed but provides no explicit guidance on when to use this tool versus alternatives like get_segment or list_segment_stats. No when-not-to-use or alternative tool mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses output formats (markdown and/or DOCX) and mentions sections, but does not confirm read-only behavior, prerequisites, error handling, or what happens if the dataset is missing. More behavioral context would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single, dense sentence that conveys the essential purpose and outputs. It front-loads key terms (executive-ready, orchestrated). While efficient, breaking into structured points could improve scanability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 10 optional parameters and no output schema, the description provides a clear overview of what the report includes: health rating, strengths/gaps, recommendations, and specific sections. However, it does not detail return values or structure, leaving some gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for all 10 parameters, providing detailed descriptions. The tool description adds a high-level summary of the report contents but does not elaborate on parameters beyond what the schema already offers. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool produces an 'executive-ready orchestrated dataset audit' with a narrative assessment, health rating, strengths/gaps, recommendations, and sections on governance, consumption, and settings. This specific verb+resource combination distinguishes it from sibling 'audit_dataset' by emphasizing 'orchestrated' and 'executive-ready'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies use for a comprehensive, high-level audit but does not explicitly state when to use this vs. simpler alternatives like 'audit_dataset' or other sibling tools. No when-not-to or context exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only states listing behavior; no mention of pagination, permissions, or side effects. For a simple list operation, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no filler, directly conveys the tool's function and optional filtering.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and one simple optional parameter, description provides basic functionality. However, it lacks any indication of return format (e.g., list of segment names/IDs), leaving incomplete understanding for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and parameter description in schema is clear. Description adds no further detail beyond schema, meeting baseline but not surpassing it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'List' and specific resource 'segments across all Interaction Studio datasets, or filter to a single dataset.' Distinguishes from sibling 'list_segments' by indicating cross-dataset scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states two use cases: listing all segments or filtering by dataset. However, lacks explicit when-not-to-use or comparison with similar tools like 'list_segments'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states what the tool returns but does not explicitly disclose whether it is a read-only operation, authentication requirements, or error behavior. The read-only nature is implied by 'Get', but not guaranteed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence of 12 words that immediately conveys the purpose. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided; the description gives some insight into return contents ('state, targeting, and experiences') but lacks full structure. However, for a simple read tool, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so baseline is 3. The description adds 'state, targeting, and experiences' which hints at returned details but does not clarify parameter meanings beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb 'Get', resource 'a single Interaction Studio campaign', and specifics 'including state, targeting, and experiences'. This distinguishes it from siblings like 'list_campaigns' (list vs single) and 'get_campaign_stats' (different details).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for fetching details of a specific campaign, but it does not provide explicit guidance on when to use it versus alternatives like 'get_campaign_context' or 'get_campaign_stats', nor does it mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the scanning and resolution behavior, but lacks information on whether the tool is read-only, authentication needs, or potential side effects. Additional context like 'read-only' or 'no modifications' would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: first sentence states action and method, second adds key clarification. Information is front-loaded and efficiently communicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 4 optional parameters, the description is mostly complete for a mapping tool. However, it does not specify the output format (e.g., returns a list of mappings or a JSON structure), which would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add meaningful parameter semantics beyond what the schema already provides (e.g., clarifying default values or usage constraints for each parameter).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Map' and resources 'recommendation recipes to campaigns', distinguishing it from sibling tools like list_campaigns or get_campaign. It also explains the method 'by scanning experience configuration' and the unique function of resolving the API inUse flag.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to reconcile API flags with actual campaign linkages, but provides no explicit guidance on when to use it versus siblings like list_campaigns or get_campaign. No when-not-to-use or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description confirms it is a read-only listing operation. However, it does not explicitly state it is non-destructive or mention any permissions or rate limits, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no fluff; purpose and output fields are front-loaded. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a listing tool with no parameters and no output schema, the description adequately explains what is returned. It could mention if results are limited or paginated, but overall it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters, so description does not need to add parameter details. Baseline score of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all Interaction Studio datasets and what fields are returned. It distinguishes itself from sibling 'get_dataset' which retrieves a single dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'get_dataset'. Usage is implied from the description: use when you need a list of all datasets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the requirement for confirm and the dry_run default, which imply mutability. However, it does not explain what happens on execution, error conditions, or rate limits. It adds some behavioral context but is incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with the core purpose, followed by essential usage prerequisites. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and moderate complexity, the description is adequate for basic usage but lacks information about return values, success/failure indicators, or error handling. It does not fully compensate for missing structured output information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 adds value by explaining the relationship between confirm, dry_run, and execution, clarifying that confirm must be true to execute, which is not fully captured in the parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Enable or disable' and the resource 'Interaction Studio segment'. This distinguishes it from sibling tools like list_segments and get_segment, which are read-only.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions prerequisites (IS_WRITES_ENABLED=true and confirm=true) and the default dry_run behavior, providing clear context for when to use the tool. However, it does not explicitly state when not to use it or list alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It states the operation is a state change, requires confirmation to execute, and defaults to a preview (dry_run) mode. This adequately informs the agent about the tool's behavior, though it could mention potential side effects (e.g., campaign visibility changes).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences that cover purpose, prerequisites, and default behavior. No unnecessary words or repetition. It is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a state-changing tool with 5 parameters and no output schema, the description covers the main action, prerequisites, and default behavior. However, it does not explain what happens after execution (e.g., return value or success indication), which would be helpful. Still, it is largely complete given the tool's context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all 5 parameters with descriptions (100% coverage). The description adds high-level context (confirm required, dry_run default) but largely restates what the schema provides. Baseline 3 is appropriate; the description does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: to publish or disable an Interaction Studio campaign by updating its state. It specifies the exact actions (Published or Disabled) and the resource (campaign). This distinguishes it from sibling tools like list_campaigns or set_segment_enabled.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear conditions for use: requires IS_WRITES_ENABLED=true and confirm=true for execution, and defaults to dry_run for preview. It implicitly guides when to use (publish/disable) but does not explicitly state when not to use or list alternatives. However, the context of sibling tools makes the usage scope clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the tool validates session and lists datasets, but does not specify return format or behavior on invalid session, leaving ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states purpose, second gives when-to-use guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so description should clarify return values. It mentions validating and listing, but does not detail what 'validate' returns (e.g., boolean, error). Still, the tool is simple and the description is sufficient for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so coverage is trivially 100%. Baseline for 0 params is 4, and description adds no parameter info, which is acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it validates the Interaction Studio session and lists datasets. This distinguishes it from sibling tools which deal with content, campaigns, segments, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to use this tool first when encountering 401 errors, providing clear and actionable usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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