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 states the tool 'estimates' but does not disclose behavioral traits such as input constraints (e.g., text length, language), output format (though an output schema exists), error handling, or performance characteristics. This is a significant gap 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 and front-loaded: two sentences that directly state the tool's purpose without unnecessary details. Every word earns its place, making it efficient for an AI agent to parse.

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 (a readability metric), no annotations, and an output schema (which handles return values), the description is minimally adequate. It states what the tool does but lacks context on usage, parameters, and behavior, leaving gaps that the output schema alone does not fill.

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?

The input schema has 1 parameter with 0% description coverage. The description does not add any semantic details about the 'text' parameter (e.g., format, language requirements, length limits). With low schema coverage, the description fails to compensate, but the single parameter is straightforward, so a baseline 3 is appropriate.

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: 'Estimates years of formal education needed to understand text.' It specifies the verb ('estimates') and resource ('text'), but does not explicitly differentiate from siblings like 'flesch_kincaid_grade' or 'smog_grade_index' which also assess readability/education levels, though it implies a specific metric (Gunning Fog Index).

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. It does not mention sibling tools (e.g., 'flesch_reading_ease', 'coleman_liau_index') or contexts where the Gunning Fog Index is preferred, leaving the agent without explicit usage instructions.

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