blackmount-nlp-mcp

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

With no annotations provided, the description carries full burden but only states the basic operation. It lacks details on behavioral traits such as how stopwords are defined, whether removal is case-sensitive, what happens with non-English text, or the output format, which is insufficient for a mutation tool.

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 with zero waste—it directly states the tool's function and scope ('500+ built-in') without unnecessary elaboration, making it appropriately sized and front-loaded.

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 moderate complexity (text processing with mutation), no annotations, and an output schema present (which covers return values), the description is minimally adequate. However, it lacks details on behavioral aspects like error handling or performance, leaving gaps in completeness.

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 adds meaning by specifying that the 'text' parameter is processed to remove English stopwords, which clarifies the parameter's purpose beyond the schema's minimal coverage (0%). Since there's only one parameter, this compensates well, though it doesn't detail input constraints like text length.

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 'remove' and the resource 'English stopwords from text', specifying the scope of '500+ built-in' stopwords. It distinguishes from siblings like clean_remove_emails or clean_remove_punctuation by focusing on stopwords, though it doesn't explicitly contrast them.

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. It doesn't mention prerequisites (e.g., text should be in English), exclusions, or compare to siblings like clean_text_pipeline that might include this operation, leaving usage context implied.

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