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 mentions the method ('Counts polysyllabic words') but lacks details on output format, accuracy, limitations, or how the grade is calculated. For a tool with no annotations, this leaves significant behavioral 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 with two short sentences that are front-loaded and waste no words. Every part ('SMOG Grade', 'Best for healthcare/medical texts', 'Counts polysyllabic words') adds value without redundancy.

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 one parameter, no annotations, and an output schema (which handles return values), the description is minimally adequate. It covers purpose and usage but lacks details on behavior and parameters, making it incomplete for optimal agent understanding without relying heavily on the output schema.

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 implies the 'text' parameter is for inputting healthcare/medical texts but doesn't elaborate on format, length constraints, or language requirements. This adds minimal meaning beyond the schema's basic structure.

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: 'SMOG Grade' indicates it calculates a readability metric, and 'Counts polysyllabic words' specifies the method. It distinguishes from siblings like 'flesch_reading_ease' or 'gunning_fog_index' by naming the SMOG algorithm, but doesn't explicitly contrast 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?

It provides clear context with 'Best for healthcare/medical texts', guiding when to use this tool. However, it doesn't specify when not to use it or name alternatives among siblings, such as suggesting other readability indices for different text types.

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