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 mentions TF-IDF computation 'from scratch' and the benefit of multiple documents, but fails to disclose critical behavioral traits such as performance implications, input format expectations, error handling, or output structure. For a tool with no annotation coverage, this is insufficient.

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 brief and front-loaded with the core purpose in the first sentence, followed by a usage tip. It avoids unnecessary words, but could be slightly more structured (e.g., separating purpose from guidelines). Overall, it's efficient with little waste.

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 (2 parameters, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and a usage tip, but lacks details on parameters, behavioral context, and output expectations. The presence of an output schema mitigates some gaps, but overall it's only minimally adequate.

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 description must compensate. It only vaguely references 'multiple docs,' which relates to the 'documents' parameter, but provides no details on document format, content requirements, or the 'top_n' parameter's role. This adds minimal value beyond the bare schema, failing to adequately explain parameter meanings.

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: 'Extract keywords using TF-IDF computed from scratch.' It specifies the verb ('extract'), resource ('keywords'), and method ('TF-IDF computed from scratch'), which distinguishes it from sibling tools like 'extract_rake_keywords'. However, it doesn't explicitly differentiate from other keyword extraction methods beyond mentioning TF-IDF, keeping it at a 4 rather than a 5.

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 some implied usage guidance with 'Pass multiple docs for best results,' suggesting optimal conditions. However, it lacks explicit when-to-use rules, alternatives (e.g., vs. 'extract_rake_keywords'), or exclusions. This leaves gaps in guiding the agent on tool selection among siblings.

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