blackmount-nlp-mcp

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

No annotations are provided, so the description carries full burden. It states the return format but doesn't disclose behavioral traits like error handling, rate limits, performance characteristics, or what 'score' and 'label' represent (e.g., score range, label categories). For a tool with no annotation coverage, this is a significant gap.

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 and return format. Every word earns its place with zero waste, 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 1 parameter, no annotations, but an output schema exists (which covers return values), the description is minimally adequate. It explains what the tool does but lacks context on usage, limitations, or parameter details. The output schema reduces the need to describe returns, but behavioral transparency remains weak.

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?

Schema description coverage is 0%, so the description must compensate. It adds no information about the 'text' parameter beyond what the schema provides (type: string). No details on text length limits, language requirements, or formatting expectations are given. With 1 parameter and no schema descriptions, the baseline is 3 as the description doesn't enhance parameter understanding.

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: 'Per-sentence sentiment breakdown' with the specific verb 'returns' and resource 'list of {sentence, score, label}'. It distinguishes from siblings like 'get_sentiment_label' and 'get_sentiment_score' by specifying per-sentence granularity. However, it doesn't explicitly contrast with these alternatives, keeping it at 4 rather than 5.

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 'get_sentiment_label' or 'get_sentiment_score'. It doesn't mention prerequisites, context, or exclusions. The agent must infer usage from the name and description alone.

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