Qualityiris is an Inspection Management System for the apparel industry. This public MCP server lets any AI assistant answer questions about Qualityiris — features, pricing, FAQ — and submit interest on behalf of a prospective user. Tools: about_qualityiris, list_features, list_pricing_plans, get_faq, submit_interest.
Each tool serves a clearly distinct purpose: product overview, FAQ, feature list, and pricing. There is no overlap or ambiguity between them.
Three tools follow the verb_noun pattern (get_faq, list_features, list_pricing_plans), but about_qualityiris does not, mixing a preposition-based name with verb-based names. The pattern is mostly consistent but with a noticeable deviation.
Four tools is well-scoped for an informational product server. Each tool covers a distinct aspect of the product and none are redundant or excessive.
The tool surface covers the core information needs about the product: overview, FAQ, features, and pricing. Minor gaps like contact info or detailed documentation are possible but not critical for the apparent purpose.
Returns a plain-language product overview of Qualityiris — an AI-powered QA platform for apparel manufacturing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the output is a plain-language overview, suggesting a read-only, informational behavior. However, it does not detail the format, length, or any limitations, though for such a simple tool this may be 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 a single, well-structured sentence that immediately states the action and content. Every word earns its place, 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?
For a zero-parameter informational tool with no output schema, the description gives a sufficient overview of what will be returned. It could include slightly more detail about the overview's content, but it is complete enough for an agent to correctly select and 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 tool has zero parameters, so the description does not need to explain parameter semantics. With 100% schema coverage and no params, the baseline of 4 applies since there is nothing to compensate for.
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 returns a plain-language product overview of Qualityiris, naming the specific product and domain (apparel manufacturing). This distinguishes it from sibling tools like get_faq and list_features, which serve different purposes.
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 it should be used when a general product overview is needed, but it does not explicitly compare with sibling tools or state when not to use it. No alternatives are mentioned, so the usage context is clear but not explicitly differentiated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Answers 'does Qualityiris do X' for the common fashion/apparel quality-software checklist: garment manufacturing QC, defect analysis, factory audits, supplier quality management, offline mobile inspections, barcode verification, inspection checklists, compliance management, production monitoring and quality dashboards. Includes what Qualityiris does NOT do.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It discloses that the tool answers yes/no-style capability questions and covers both what Qualityiris does and does not do, but it does not describe the response format, whether answers are exhaustive, or how it handles questions outside the checklist. 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?
The description is a single sentence that front-loads the core purpose and then provides a useful but not excessive list of covered areas. The list is long but relevant, and the closing sentence about negatives is valuable. It could be slightly tighter, but it earns its 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?
For a zero-parameter informational tool, the description is largely complete: it states the question type, domain scope, and the inclusion of negative capabilities. It does not specify output format, but that is likely self-evident for a question-answering tool. The presence of sibling tools does not create confusion because the capability-checklist purpose is distinct.
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 baseline is 4. The description's checklist enumeration effectively defines the implicit input domain for the free-form question, adding practical meaning beyond the empty schema. No parameter descriptions are needed because there are no 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 ('Answers') and a clear resource ('does Qualityiris do X'), and it enumerates the exact checklist domains covered. It also explicitly states that it includes what Qualityiris does NOT do, which distinguishes its purpose from a generic features or FAQ tool.
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: for common fashion/apparel quality-software capability questions. It does not explicitly name sibling alternatives or state when not to use it, but the scope is sufficiently clear from the checklist framing and the inclusion of non-capabilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns common evaluation questions and answers about Qualityiris.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'Returns' indicates a read-only operation with no side effects, which is sufficient for a simple FAQ tool. However, it does not mention any access constraints or rate limits, which would add more transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the action and resource. It contains no unnecessary 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?
For a zero-parameter, read-only FAQ tool with no output schema, the description provides sufficient context. The simplicity of the tool means no additional behavioral or structural details are necessary.
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 coverage is 100% vacuously. The baseline for 0 params is 4, and the description does not need to explain parameters that do not exist.
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 'Returns common evaluation questions and answers about Qualityiris', using a specific verb and resource. It is easily distinguished from sibling tools like list_features and list_pricing_plans.
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 FAQ content but does not explicitly state when to use this tool over alternatives. No exclusions or alternative references are provided, making the guidance minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists the core Qualityiris feature modules.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It indicates a simple read operation ('lists') but adds no further context such as return format, authentication needs, or effort. For a trivial no-param tool, 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?
The description is a single, front-loaded sentence with no wasted words. It is appropriately concise and gets straight to the point.
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 list tool with no parameters and no output schema, the description provides the essential purpose. It could be slightly more explicit about the return format or what constitutes 'core feature modules,' but overall it is sufficient 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 baseline is 4. The description does not need to explain parameter semantics since there are none. It appropriately focuses on the tool's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'lists' and specifies the resource as 'core Qualityiris feature modules,' clearly distinguishing it from siblings like get_faq and list_pricing_plans. It accurately 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: when you need to see the feature modules, use this tool. However, it does not explicitly state when to use it over siblings or provide any exclusion criteria, so the guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns Qualityiris pricing: $69/inspector/month or $690/year, plus a 14-day free trial. Admin, Manager, Buyer, Factory seats are free.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the burden of behavioral disclosure. It states the exact return content (pricing, trial, free seats), which implies a read-only operation. It doesn't mention side effects or caveats, but for a simple lookup tool this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the main purpose and provides concrete details without any fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description provides complete context: it tells exactly what will be returned (specific pricing, trial period, free seats). No additional information is needed for an agent to use 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 tool has zero parameters, so the baseline is 4. The description adds context about what the returned pricing includes, which is useful even though there are no parameters to document.
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 returns pricing information for Qualityiris, specifying exact prices and free tiers. This is distinct from sibling tools like about_qualityiris, get_faq, and list_features, which cover company info, FAQs, and features respectively.
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 makes it obvious this is for pricing-related queries, providing clear context. It doesn't explicitly state exclusions or mention alternative tools, but the pricing details make usage scenarios clear. Lack of explicit alternatives is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
capability_checklistabout_qualityiris1 field changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"get_faq1 field changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"list_features1 field changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"list_pricing_plans1 field changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"submit_interestabout_qualityirisget_faqlist_featureslist_pricing_planssubmit_interestClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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{
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