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 action ('Remove email addresses') but doesn't disclose behavioral traits such as whether the removal is case-sensitive, what happens to malformed emails, if it handles international domains, or the output format. This leaves significant gaps for an agent to understand how the tool behaves beyond the basic operation.

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, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core function without unnecessary elaboration, making it easy for an agent to parse quickly.

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 parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, without annotations and with incomplete parameter semantics, it lacks details on behavioral aspects and usage context, leaving room for improvement in guiding the agent effectively.

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 minimal meaning beyond the input schema, which has 0% coverage. It implies the 'text' parameter is the input from which emails are removed, but doesn't specify constraints like length limits, encoding, or examples. With only one parameter and low schema coverage, the description provides basic context but doesn't fully compensate for the lack of schema details.

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 verb ('Remove') and resource ('email addresses from text'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'clean_remove_urls' or 'clean_remove_html', which follow similar patterns but target different content types.

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. With siblings like 'clean_remove_urls' and 'clean_remove_html' available, there's no indication of whether this tool is for preprocessing text, data cleaning, or specific contexts where email removal is needed over other cleaning operations.

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