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 discloses the return format ('Returns [{phrase, count}]') and default values, which is helpful. However, it lacks critical behavioral details: it doesn't specify if the tool is read-only, how it handles edge cases (e.g., empty text), whether it's case-sensitive, or if there are performance constraints. The description adds some value but leaves significant gaps for a tool with no annotation coverage.

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—just two short sentences—with zero wasted words. It front-loads the key information (purpose and defaults) and efficiently includes the return format. Every sentence earns its place by adding distinct value.

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 that there's an output schema (which likely documents the return structure), the description doesn't need to fully explain return values. However, with no annotations and 0% schema description coverage, the description should provide more behavioral context (e.g., safety, performance) and parameter guidance. It's adequate for a simple analysis tool but lacks depth for robust agent use.

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 effectively explains the core parameters: 'n-grams (phrases)' clarifies the 'n' parameter, 'Default bigrams' provides the default for 'n', and 'most frequent' and 'Top N' (implied) relate to 'top_n'. However, it doesn't explicitly mention the 'text' parameter or provide examples or constraints (e.g., text length limits). Given the low schema coverage, the description does a good job but misses one parameter.

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: to find 'most frequent n-grams (phrases)' and specifies the default behavior ('Default bigrams'). It distinguishes from siblings like 'get_word_frequency' by focusing on phrases rather than single words. However, it doesn't explicitly contrast with 'generate_ngrams' or 'generate_char_ngrams', which are related sibling 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 provides no guidance on when to use this tool versus alternatives like 'get_word_frequency', 'generate_ngrams', or other text analysis tools in the sibling list. It mentions 'Default bigrams' but doesn't explain when to choose different n-values or when this tool is preferable over other frequency or n-gram tools.

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