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. It mentions the VADER-style method and lexicon size, which adds some behavioral context, but fails to disclose critical details like rate limits, error handling, or performance characteristics. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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: the output (compound sentiment score with range), method (VADER-style), and lexicon detail. Every word earns its place with no redundancy or 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 (which handles return values), the description is reasonably complete. It covers the purpose and method well, but could improve by adding usage guidelines relative to siblings and more behavioral details, especially since no annotations are provided.

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, but the tool only has one parameter ('text'). The description doesn't explicitly mention parameters, but the context is clear from the tool's purpose: it processes text for sentiment. Since there's only one straightforward parameter, the description adequately implies its use without needing detailed param info, compensating for the low schema coverage.

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 calculates a 'compound sentiment score' using VADER-style analysis with a specific lexicon. It specifies the output range (-1 to 1) and mentions the method, but doesn't explicitly distinguish it from sibling tools like 'get_sentiment_label' or 'get_sentence_sentiments' beyond the compound score aspect.

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?

No explicit guidance on when to use this tool versus alternatives like 'get_sentiment_label' or 'get_aspect_sentiment' is provided. The description implies usage for sentiment analysis but lacks context on specific scenarios or exclusions, leaving the agent to infer based on tool names alone.

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