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 of behavioral disclosure. It states the tool returns a grade level, but doesn't describe output format, potential errors, rate limits, or computational requirements. While it implies a read-only operation, it lacks details on what the grade level represents (e.g., numeric score, interpretation). 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: one sentence that directly states the tool's function. Every word earns its place, with no redundancy or unnecessary details. It efficiently communicates the core purpose without waste.

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 low complexity (one input parameter) and the presence of an output schema, the description is minimally adequate. It covers the basic purpose but lacks behavioral details and usage guidelines. With no annotations, it should do more to explain output format or error handling, but the output schema may mitigate this. It's complete enough for a simple readability tool 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?

The description adds no parameter information beyond what the input schema provides. Schema description coverage is 0%, but the single parameter 'text' is self-explanatory. The description doesn't compensate for the coverage gap by explaining input expectations (e.g., text length, language). Baseline is 3 since the schema covers the parameter adequately, but no extra semantic value is added.

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: 'Returns US school grade level needed to understand text.' It specifies the verb ('returns') and resource ('US school grade level'), and distinguishes it from siblings like 'flesch_reading_ease' or 'get_reading_level' by naming the specific metric. However, it doesn't explicitly differentiate from all siblings, such as 'gunning_fog_index' or 'smog_grade_index', which are also readability metrics.

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 doesn't mention when to choose Flesch-Kincaid over other readability tools like 'coleman_liau_index' or 'gunning_fog_index', nor does it specify prerequisites or exclusions. Usage is implied by the tool's name and purpose, but explicit context is missing.

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