colacloud-mcp

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

With no annotations provided, the description carries the full burden. It discloses that barcodes are extracted from label images using computer vision, which adds useful context about data source and limitations. However, it doesn't cover behavioral aspects like error handling, rate limits, authentication needs, or what happens if no matches are found.

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 well-structured and appropriately sized. It front-loads the core purpose, follows with implementation context, and ends with clear sections for Args and Returns. Every sentence earns its place without redundancy.

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 moderate complexity (single parameter, no annotations, but with output schema), the description is mostly complete. It explains the purpose, parameter semantics, and return value at a high level. The existence of an output schema means it doesn't need to detail return structure, but could benefit from more behavioral context for a lookup 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 description adds significant meaning beyond the input schema, which has 0% coverage. It explains that barcode_value accepts UPC, EAN formats with an example ('012345678905'), clarifies what constitutes a barcode, and mentions it's extracted from label images. This compensates well for the schema's lack of descriptions.

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 purpose with specific verbs ('Find', 'identifying', 'tracking') and resources ('COLAs by product barcode'). It distinguishes from siblings by focusing on barcode-based lookup rather than general searches or other entity retrievals like get_cola or search_colas.

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 clear context for when to use this tool ('useful for identifying products or tracking label changes over time'), but does not explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage based on barcode availability.

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