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. While it states what the tool calculates, it doesn't describe the output format, whether it returns a numeric score or grade level label, error conditions, or performance characteristics. For a calculation tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 just two sentences that communicate the essential information. The first sentence names the tool and its purpose, the second explains the calculation basis. Every word earns its place with zero waste or 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 the tool has an output schema (which should document return values), the description covers the basic purpose adequately. However, for a calculation tool with no annotations, it should ideally mention something about the output format or typical use cases to provide better context for agent decision-making.

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?

With only 1 parameter and 0% schema description coverage, the description compensates well by explaining that the tool uses 'character and word counts' from the input text. This provides meaningful context about what the 'text' parameter will be analyzed for, though it doesn't specify text length requirements or format expectations.

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 calculates the Automated Readability Index (ARI) to determine grade level from character and word counts, providing a specific verb (calculate/grade) and resource (readability index). It distinguishes from siblings by focusing on ARI specifically rather than other readability formulas like Flesch-Kincaid or Coleman-Liau.

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 'flesch_kincaid_grade', 'coleman_liau_index', or 'get_reading_level'. It doesn't specify what makes ARI unique or when it's most appropriate compared to other readability metrics available in the sibling tools.

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