merit_list_customers

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

With no annotations provided, the description carries full behavioral disclosure. It warns that unfiltered calls may return very large lists or even fail to serialize, and it documents exact vs partial matching and ISO date formats. This meaningfully exceeds bare schema information.

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?

Four compact sentences, front-loaded with the tool's purpose and endpoint, followed only by high-value caveats and matching rules. There is no filler or redundant restating of the parameter schema.

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 read/list tool with no output schema and no annotations, the description covers the key operational concerns: default behavior, scale risks, filter precedence, matching semantics, and date formatting. The parameter descriptions fill in the remaining field-level details.

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 critical semantic value beyond the schema: id wins over all filters, regNo/vatRegNo are exact matches while name is partial, and date format is ISO. These interaction rules are not evident from the schema 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 uses a specific verb-resource pair ('List/search Merit Aktiva customers') and includes the exact endpoint. It is clearly distinct from sibling tools like create_customer or customer debt reports.

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?

It gives strong situational guidance: use filters to avoid huge result sets, and explains filter precedence and matching behavior. It does not explicitly contrast with alternative retrieval tools, but the filter guidance is clear enough for most selection decisions.

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