get_news_sentiment

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

Discloses fallback mechanism (Tiingo vs LLM), but no annotations are provided. Lacks details on rate limits, latency, cost implications, or behavior on failure. For a tool without annotations, more behavioral context would be beneficial.

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?

Two succinct sentences: first states core function, second adds method detail. No filler, information is front-loaded.

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?

Covers purpose and method, but does not describe return format or how the sentiment is presented. Output schema likely covers this, but the description could briefly mention output nature (e.g., score, label). Still sufficient for a simple retrieval tool.

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 coverage is 100%, so the description adds limited value beyond the schema. It mentions the method context but does not enhance parameter understanding. Baseline of 3 is appropriate.

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?

Description clearly states 'Get news sentiment analysis for a stock', specifying verb (get), resource (news sentiment), and target (stock). It also mentions the method (Tiingo News API or LLM with fallback), distinguishing it from siblings like data_get_stock_info or technical tools.

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 vs alternatives like research_analyze_market_sentiment or data_get_news_sentiment. Does not mention when not to use it or any prerequisites.

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