VeChain 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. It describes the conversion behavior but lacks details on error handling (e.g., invalid inputs), performance traits (e.g., rate limits), or output format (e.g., numeric string). For a tool with no annotation coverage, this is a significant gap in behavioral disclosure.

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, efficient sentence that front-loads the core purpose with a clarifying example. There is no wasted text, making it highly concise and well-structured for quick understanding.

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 (involving token conversions with 2 parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It lacks details on parameter usage, error cases, and output format, which are essential for effective tool 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 description coverage is 0%, so the description must compensate. It mentions 'token amount' and 'human-readable units', which loosely relates to the 'amount' parameter, but doesn't explain the 'tokenAddress' parameter or its pattern. With 2 parameters and low coverage, the description adds minimal semantic value beyond the schema.

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 verb 'convert' and the resource 'token amount', specifying the transformation from 'human-readable units to its smallest unit' with an example 'wei'. However, it doesn't explicitly differentiate from its sibling 'convert_from_base_units', which performs the inverse operation, leaving some ambiguity about when to use each.

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?

No guidance is provided on when to use this tool versus alternatives. While the description implies usage for converting token amounts, it doesn't mention prerequisites (e.g., token address validation), exclusions, or compare it to sibling tools like 'convert_from_base_units', leaving the agent to infer context.

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