summarize_news

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

No annotations are provided, so the description carries the full burden. It discloses the AI model used (Groq/LLaMA with NVIDIA NIM fallback) and lists the return fields. However, it does not mention potential limitations like hallucination risk, cost, or rate limits, which would improve transparency.

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 three sentences: first states purpose, second lists returns, third mentions tech stack. It is concise and front-loaded, but could be more structured (e.g., bullet points for return fields improve scannability).

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 tool with no output schema, the description adequately explains what the tool does and returns. It covers the AI model, input parameters, and output fields. Missing details like error handling or behavior on invalid symbols, but complete enough for an agent to use.

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 100%, so the schema already documents both parameters. The description adds minimal extra meaning—only example symbols in parentheses and a default value mention. Baseline 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?

The description clearly states the tool's purpose: 'AI-powered concise briefing of the latest news for a stock.' It lists specific return fields (sentiment, themes, narrative, catalysts, risks), and distinguishes from sibling tools like get_news (which likely returns raw news) and get_market_news (broader market news).

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 implies usage for stock news summarization but provides no explicit guidance on when to use this tool versus alternatives such as get_news or get_market_news. No exclusions or prerequisites are mentioned.

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