LabelChop MCP Server

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

No annotations are provided, so the description should disclose behavioral traits. It only states the tool diagnoses and recommends, but does not mention output format, reasoning process, or any side effects. This leaves significant ambiguity.

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 wasted words. It is front-loaded with the core purpose. While it could benefit from a bit more detail, it remains appropriate in size.

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 5 parameters, no output schema, and sibling tools, the description lacks thorough guidance. It does not explain the recommendation logic, parameter importance, or distinguish from 'recommend_label_printing_setup', leaving gaps for effective tool selection and invocation.

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

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

Schema coverage is 100%, so the schema already describes all 5 parameters thoroughly. The description adds no additional parameter meaning, earning the baseline score of 3.

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

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

The description clearly states the tool diagnoses a shipping-label workflow and recommends between two specific solutions (LabelChop vs A4-to-4x6 converter). This distinguishes it from sibling tools like 'find_labelchop_resources' and 'recommend_label_printing_setup'.

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 is for sellers with label printing issues but provides no explicit when-to-use or when-not-to-use guidance, nor does it compare against sibling tools. 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?

No annotations are provided, so the description carries full burden. It conveys a non-destructive search/retrieval function, but does not disclose behaviors like handling of no results, multiple matches, or any rate limits. Adequate for a simple query tool.

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 packs many examples, making it slightly dense but still relatively concise. It front-loads the purpose and examples; splitting into two sentences could improve readability without adding length.

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 required param, no output schema), the description covers the purpose, scope, and example inputs. It does not explain the return format or behavior for empty results, but for a search tool, this is often inferred. 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?

Schema description coverage is 100% for the single required param 'query' described as 'What the user is trying to solve'. The description adds value by listing example topics (A4, 4x6, MyPost Business, etc.), giving the agent concrete context beyond the generic schema description.

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

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

The description clearly states the tool finds LabelChop resource links for specific contexts like A4 labels, thermal printing, and various platforms. It distinguishes from siblings 'diagnose_shipping_label_workflow' and 'recommend_label_printing_setup' which focus on diagnosis and recommendation, not resource finding.

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 context (when to find resource links for listed topics) but does not explicitly state when not to use the tool or mention alternatives. With sibling tools present, the lack of delineation is a slight gap; the agent must infer the boundary.

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?

No annotations are provided, so the description must bear full burden. It only states 'recommend' without disclosing what the recommendation includes, how it's generated, or any constraints, which is insufficient for a recommendation tool.

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 extraneous words, efficiently conveying the tool's 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?

The tool has 4 optional parameters and no output schema; the description fails to specify what the recommendation contains (e.g., specific printers, settings, steps), leaving the agent without enough context to use the tool effectively.

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%, but the description adds no additional meaning beyond parameter names and basic descriptions in the schema, such as how platform or printerModel affect the recommendation.

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 recommends a practical 4x6 thermal-label printing setup for Australian ecommerce sellers, using a specific verb and resource with constraints, and distinguishes from sibling tools like diagnose_shipping_label_workflow.

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

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

While the purpose implies when to use (e.g., needing a recommendation), the description lacks explicit guidance on when not to use or alternatives, leaving the agent to infer from sibling names only.

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