blackmount-nlp-mcp

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 only states the basic function without mentioning any behavioral traits such as error handling (e.g., for non-string inputs), performance characteristics, or side effects. This is inadequate for a tool with no annotation coverage.

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, direct sentence with no wasted words, making it highly concise and front-loaded. Every part of the sentence ('Convert text to lowercase') contributes essential information about the tool's purpose.

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 low complexity (one parameter, simple function) and the presence of an output schema (which likely handles return values), the description is minimally complete. However, it lacks context on usage, behavior, and sibling differentiation, which are gaps for a tool with no annotations.

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 implies the parameter 'text' is the input to be converted, adding meaning beyond the schema which has 0% description coverage. Since there is only one parameter, the baseline is 4, as the description compensates adequately for the lack of schema details by clarifying the parameter's role.

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 'Convert text to lowercase' clearly states the tool's function with a specific verb ('Convert') and resource ('text'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'clean_normalize_whitespace' or 'clean_remove_punctuation' that also perform text cleaning operations, though the naming convention suggests a family of cleaning tools.

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 use cases (e.g., text normalization for analysis), prerequisites, or comparisons to other cleaning tools in the sibling list, leaving the agent to infer usage based on the name alone.

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