blackmount-nlp-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it indicates the tool returns comprehensive reading analysis, it doesn't describe what 'comprehensive' means operationally, whether there are computational limitations, what specific readability scores are included, or how the grade level and label are determined. For a tool with no annotation coverage, 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 a single, efficient sentence that front-loads the key information: 'Comprehensive reading level' followed by specific outputs. There's no wasted language, though it could potentially benefit from slightly more detail given the tool's apparent complexity and the lack of annotations or output schema.

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 complexity of reading level analysis, the absence of annotations, no output schema, and 0% schema description coverage, the description is insufficiently complete. It doesn't explain what specific metrics are included in 'all readability scores', how the grade level is calculated, what the label categories mean, or what format the output takes. For a tool that appears to synthesize multiple readability metrics, more contextual information is needed.

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 0% description coverage, showing only a 'text' parameter with no explanation. The description doesn't mention parameters at all, so it adds no semantic value beyond what the bare schema provides. With only one parameter, the baseline is 4, but since the description provides zero parameter information, it doesn't meet that baseline and earns a 3 for failing to compensate for the schema's lack of documentation.

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 provide comprehensive reading level analysis including grade level, label categorization, and all readability scores. It uses specific verbs ('get', 'provide') and identifies the resource ('reading level'), though it doesn't explicitly distinguish from sibling tools like 'flesch_kincaid_grade' or 'automated_readability_index' that appear to provide specific readability metrics rather than comprehensive analysis.

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 the many sibling tools available. With numerous related tools like 'flesch_kincaid_grade', 'automated_readability_index', 'gunning_fog_index', and 'get_text_statistics', there is no indication of when this comprehensive tool is preferable to individual metric tools or what distinguishes its output from other analysis tools on the server.

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