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 mentions the tool 'finds sentences' and 'averages their sentiment,' which implies a read-only analysis operation, but does not specify output format, error handling, performance limits, or other behavioral traits. This leaves significant gaps 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 highly concise and front-loaded, using two clear sentences that directly state the tool's function and method without any wasted words. Every sentence earns its place by contributing essential information efficiently.

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 no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It covers the basic purpose but lacks details on parameters, behavior, output format, and usage context. For a tool with 2 parameters and complex sentiment analysis, this leaves the agent under-informed.

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 for undocumented parameters. It implies that 'text' is the input to analyze and 'aspects' are topics to evaluate, but does not explain parameter formats, constraints, or examples. With 2 parameters and no schema descriptions, this adds minimal semantic value beyond basic inference.

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: 'Sentiment around specific topics/aspects' indicates it analyzes sentiment, and 'Finds sentences mentioning each aspect and averages their sentiment' specifies the method. It distinguishes from siblings like 'get_sentence_sentiments' or 'get_sentiment_score' by focusing on aspect-based analysis, though it could be more explicit about the distinction.

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 like 'get_sentence_sentiments' for general sentiment or 'get_sentiment_score' for overall sentiment, leaving the agent to infer usage from the purpose alone without explicit context or exclusions.

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