blackmount-nlp-mcp

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

No annotations are provided, so the description carries full burden. It mentions handling abbreviations and tricky boundaries, which adds some behavioral context, but doesn't disclose output format, error handling, performance characteristics, or limitations. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 two concise sentences with zero waste. The first sentence states the core purpose, and the second adds crucial behavioral detail. It's front-loaded and efficiently structured, with every sentence earning its place.

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 (sentence tokenization with special handling), no annotations, and an output schema present, the description is reasonably complete. It covers the main purpose and key behavioral aspects, though it could benefit from more detail on limitations or examples. The output schema likely handles return values, reducing the need for that in the description.

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%, but the description compensates by clarifying that the 'text' parameter is the input to be split into sentences. With only one parameter, the description adds meaningful context about what the parameter represents, though it doesn't detail format constraints or examples.

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 specific action ('Split text into sentences') and resource ('text'), distinguishing it from siblings like 'word_tokenize' or 'count_sentences' by focusing on sentence-level segmentation. It explicitly mentions handling of abbreviations and tricky boundaries, which sets it apart from simpler text-splitting 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 implies usage context by specifying it handles abbreviations and tricky boundaries, suggesting it's suitable for complex text processing. However, it doesn't explicitly state when not to use it or name alternatives among siblings, such as simpler tokenizers or related tools like 'count_sentences'.

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