Find the best LabelChop resource links for A4 shipping labels, 4x6 thermal printing, MyPost Business, Australia Post, eBay, Shopify, Etsy, or label troubleshooting.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What the user is trying to solve |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
Each tool addresses a distinct task: diagnosing workflows, finding resources, and recommending hardware setups. There is no overlap in purpose.
All tool names follow a consistent snake_case verb_noun pattern (diagnose_*, find_*, recommend_*), making them predictable for an agent.
Three tools is slightly on the low side but reasonable for a narrow domain. The count feels appropriate given the focused scope.
The tool set covers the entire decision flow: diagnose the situation, find relevant resources, and recommend a setup. No obvious gaps for the server's stated purpose.
