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 for behavioral disclosure. It mentions stopword exclusion and the return format [{word, count}], which is helpful. However, it doesn't address other important behaviors: whether the tool is case-sensitive, how it handles punctuation/numbers, what constitutes a 'word', or any performance/rate limit considerations. For a text processing tool with zero annotation coverage, this leaves significant gaps.

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 (two short sentences) with zero wasted words. It's front-loaded with the core purpose, followed by the return format. Every sentence earns its place by providing essential information about what the tool does and what it returns.

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 analysis with filtering), no annotations, and an output schema that presumably documents the return format, the description provides the minimum necessary context. It states the purpose and return format, but lacks details about behavioral constraints, parameter usage, and differentiation from siblings. With an output schema handling return values, the description is adequate but has clear gaps.

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 documentation. The description adds no information about the 'text' or 'top_n' parameters beyond what's inferable from the tool name. However, since there are only 2 parameters and their names are self-explanatory ('text' to analyze, 'top_n' for result limit), the description's omission is less critical, meeting the baseline for minimal viable documentation.

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: 'Most frequent words excluding stopwords. Returns [{word, count}].' This specifies the verb (get frequency), resource (words), and key behavior (excludes stopwords). It distinguishes from siblings like 'get_phrase_frequency' by focusing on individual words, but doesn't explicitly contrast with 'clean_remove_stopwords' or 'count_words'.

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 explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention when this tool is appropriate compared to siblings like 'get_phrase_frequency', 'count_words', or 'clean_remove_stopwords'. The agent must infer usage from the purpose statement alone.

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