aspect_sentimentSentiment per aspect. Brainiall Aspect Sentiment engine.
Splits the text into sentences mentioning each aspect, classifies each, aggregates.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Input text | |
| aspects | Yes | Aspect terms to score (e.g. ['camera','battery','price']) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral detail about the internal process: sentence splitting per aspect, classification, and aggregation. It doesn't cover edge cases or return format, but it goes meaningfully beyond the annotations.
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 short and front-loaded with the core purpose, followed by a compact process summary. The 'Brainiall Aspect Sentiment engine' sentence is filler, but it doesn't materially bloat the description.
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?
For a two-parameter NLP tool, the description covers the operation well, but there is no output schema, and the description does not specify the return shape, sentiment scale, or how aggregation is represented. It is adequate for basic selection and invocation, but not fully self-sufficient.
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 schema covers both parameters fully with descriptions and an example for 'aspects'. The description adds some context by clarifying that the text is split into sentences mentioning each aspect, but it doesn't significantly change or deepen understanding of the parameters themselves, so the baseline of 3 applies.
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 identifies aspect-level sentiment classification as the core function and gives a concrete process: split text into sentences mentioning each aspect, classify, aggregate. It distinguishes itself from generic sentiment analysis through the 'per aspect' scope, though it doesn't explicitly name a sibling.
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 phrase 'Sentiment per aspect' implies the appropriate use case, but the description never says when to choose this tool over analyze_sentiment or any other sibling. No exclusions or alternatives are mentioned, so the agent must infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to distinct capabilities, and the descriptions make the intended use clear. A few adjacent pairs—extract_entities vs link_entities_to_wikidata and detect_pii vs detect_conversational_pii—require careful selection, but they are distinguishable by their stated outputs.
Names are uniformly lowercase snake_case and mostly follow a verb_object pattern, such as analyze_*, detect_*, extract_*, summarize_text, and translate_text. A few outliers like aspect_sentiment, fraud_feedback, and knowledge_ingest break the verb-first feel, but the overall pattern remains predictable.
At 22 tools, this is on the heavy side of the borderline range. Each tool has a distinct job, but the mix of core NLP, safety, fraud, health-checking, and knowledge-base management makes the surface feel sprawling rather than tightly scoped.
The core NLP coverage is broad: sentiment, toxicity, PII, entities, QA, summarization, translation, and groundedness are all present. However, the knowledge-base tools support ingest/list/query but no delete or update, creating a dead end when documents need correction or removal.