Conekta MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool creates a refund but doesn't cover critical aspects like required permissions, whether the action is reversible, rate limits, or what happens to the order after refund. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded, starting with the core purpose followed by parameter explanations. Each sentence adds value without redundancy. However, the parameter section could be more integrated into the flow rather than listed separately, slightly affecting structure.

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 complexity (a mutation with 3 parameters), no annotations, and an output schema (which reduces need to describe return values), the description is minimally adequate. It covers the basic action and parameters but lacks context on usage, behavioral traits, and error handling, leaving gaps for an AI agent.

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 0%, so the schema provides no parameter descriptions. The description adds value by briefly explaining each parameter (e.g., 'order_id: The Conekta order ID to refund'), but it doesn't provide details like format constraints, valid ranges for 'amount', or examples for 'reason'. This partial compensation earns a baseline score.

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: 'Create a refund for an order.' It specifies the verb ('Create') and resource ('refund for an order'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'cancel_order' or 'update_order', which might also modify order states, leaving some ambiguity about when to use this specific tool versus alternatives.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., order must be in a refundable state), exclusions, or comparisons to sibling tools like 'cancel_order' or 'update_order'. This lack of context could lead to misuse by an AI agent.

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