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 states what the tool does ('Remove all numbers from text') but lacks details on how it handles edge cases (e.g., decimal points, negative signs, or numbers within words), what the output looks like, or any performance considerations. This is a significant gap 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, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to parse.

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 input parameter) and the presence of an output schema (which should cover return values), the description is minimally adequate. However, with no annotations and incomplete behavioral details, it leaves gaps in understanding how the tool operates in practice, making it just sufficient for basic use.

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 tool has only one parameter ('text'), and schema description coverage is 0%, meaning the schema provides no descriptions. The description implies the parameter's purpose by stating 'Remove all numbers from text,' which adds meaningful context beyond the bare schema. However, it doesn't specify input format or constraints, keeping it from a perfect 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 with a specific verb ('Remove') and resource ('numbers from text'), making it immediately understandable. However, it doesn't explicitly distinguish itself from sibling tools like 'clean_remove_punctuation' or 'clean_remove_urls' beyond the target resource, which prevents a perfect score.

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. With many sibling tools for text cleaning and analysis, there's no mention of specific contexts, prerequisites, or comparisons to tools like 'clean_text_pipeline' that might handle multiple cleaning steps.

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