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. It discloses the scoring method (position, keyword frequency, length, title overlap), which adds behavioral context, but fails to mention critical traits like whether the tool modifies input text, handles errors, or has performance constraints (e.g., text length limits). 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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's function and scoring criteria without unnecessary details. Every sentence earns its place by providing essential information efficiently.

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 (3 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains the core functionality and scoring method, and since an output schema exists, it need not detail return values. However, it could improve by addressing behavioral aspects like error handling or input constraints to be fully comprehensive.

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%, so the schema provides no parameter descriptions. The description adds some meaning by explaining that 'n_sentences' selects the 'best N sentences' and 'title' is used for 'title overlap' scoring, but does not clarify the 'text' parameter's format or constraints. It partially compensates for the schema gap but not fully, as key details (e.g., text encoding, sentence boundaries) are omitted.

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 ('Extract') and resource ('best N sentences as a summary'), distinguishing it from sibling tools like 'get_sentiment_score' or 'get_text_statistics'. It specifies the extraction method (sentence selection) and scoring criteria (position, keyword frequency, length, title overlap), making the purpose unambiguous.

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 for extractive summarization based on scoring criteria, but does not explicitly state when to use this tool versus alternatives like 'get_sentiment_label' or 'clean_text_pipeline'. It provides context (e.g., 'Scores by position, keyword frequency, length, and title overlap') but lacks explicit guidance on scenarios or exclusions.

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