LabelChop MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it diagnoses and recommends, but does not explain how it processes inputs, what the diagnosis entails, or what form the recommendation takes (e.g., text, report). No safety or side-effect information is provided.

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, tightly-wound sentence that front-loads the action ('Diagnose') and specifies the target and likely recommendation. Every word contributes to understanding the tool's core function, avoiding any 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 5 parameters all documented in the schema and no output schema, the description is somewhat complete but leaves gaps: it does not describe the return value or how the diagnosis is delivered. For a tool with no annotations, it could mention that the output is a recommendation, but the purpose is clear enough for basic use.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning about the parameters, but the schema already documents each parameter (country, printer, problem, platform, volumePerWeek) with clear examples. No credit is lost, but no extra value is added.

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 'Diagnose' and clearly states the resource (ecommerce seller shipping-label workflow) and the outcome (recommend whether to use LabelChop or the free A4-to-4x6 converter). It distinguishes itself from sibling tools by focusing on diagnosis and recommendation between two specific options.

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 diagnosing a shipping-label workflow to get a recommendation, but it lacks explicit guidance on when to use this tool versus siblings like find_labelchop_resources or recommend_label_printing_setup. No exclusions or alternative scenarios are mentioned.

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

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It states the tool 'finds' links but does not describe output format, read-only status, pagination, or any limitations. This is minimal and leaves significant behavioral traits undisclosed.

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 that front-loads the action ('Find the best LabelChop resource links') and efficiently lists relevant topics without extraneous words. Every part contributes to understanding the tool's scope.

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 find tool, the description covers purpose and topic scope. However, it lacks differentiation from sibling tools and does not mention output format or any caveats, which is a notable gap given there is no output 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?

The schema already documents 'query' as 'What the user is trying to solve' (100% coverage). The tool description adds value by enumerating example query topics (e.g., A4 shipping labels, MyPost Business), which clarifies the expected input and compensates for abstract schema wording.

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 finds resource links for specific topics (A4 shipping labels, 4x6 thermal printing, etc.), which is a specific verb+resource. It does not explicitly contrast with sibling tools, but the action of finding links is distinct from diagnosing or recommending.

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 the user needs resource links for the listed topics, providing some context. However, it does not explicitly state when to prefer this tool over siblings like diagnose_shipping_label_workflow or recommend_label_printing_setup, nor does it offer 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions 'recommend', which implies advisory output, but does not explain what the recommendation includes (e.g., specific products, steps), how inputs affect it, or any limitations. This is minimal 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.

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

The description is a single, concise sentence that front-loads the key information: recommendation, label size, and target audience. There is no wasted verbiage; every word adds value.

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

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

Although the schema documents parameters well, the description lacks context about what the recommendation actually contains, how the inputs are used, or what the user will receive as output. With no output schema and no annotations, the description is too sparse to be considered fully complete, but it sufficiently conveys the overall purpose.

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% description coverage for all four parameters (platform, labelSize, printerModel, wantsAutomation). The tool description does not add extra meaning beyond 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.

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

The description clearly states the tool's function: it recommends a 4x6 thermal-label printing setup specifically for Australian ecommerce sellers. The verb 'recommend' is specific and the resource is well-defined, distinguishing it from sibling tools like diagnose_shipping_label_workflow and find_labelchop_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 the use case (Australian ecommerce sellers needing a label setup) but provides no explicit guidance on when to choose this tool over alternatives such as diagnosing workflows or finding resources. There are no exclusions or alternative references.

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