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. 'Count sentences in text' only states the basic function without any details on how sentences are defined (e.g., punctuation rules, handling of abbreviations), what the output format is, or potential errors (e.g., for empty text). This leaves significant gaps in understanding the tool's 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 extremely concise at three words, front-loaded with the core action, and has zero wasted words. It efficiently communicates the essential purpose without unnecessary elaboration, making it easy to parse quickly.

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 simplicity (one parameter) and the presence of an output schema, the description is incomplete. It lacks details on sentence detection logic, output format, and how it fits with sibling tools. While the output schema might cover return values, the description doesn't provide enough context for an agent to use it effectively without additional assumptions.

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 no parameter semantics beyond what the input schema provides. With 0% schema description coverage, the schema only indicates a 'text' parameter of type string. The description doesn't explain what 'text' should contain, its format, or constraints. However, since there's only one parameter, the baseline is 4, but it's reduced to 3 because the description fails to compensate for the low schema coverage by adding any meaningful context.

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 'Count sentences in text' clearly states the verb ('count') and resource ('sentences'), making the purpose immediately understandable. It distinguishes from siblings like 'count_words' or 'count_paragraphs' by specifying sentences, though it doesn't explicitly differentiate from 'sentence_tokenize' which might return sentences rather than count 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'sentence_tokenize' for counting versus listing sentences, or how it relates to other text analysis tools like 'get_avg_sentence_length'. Usage is implied by the name but not explicitly stated.

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