analyze_news_impact

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

With no annotations provided, the description fully bears the burden of behavioral transparency. It thoroughly discloses the analytical approach (impact scores, direction, classification), and includes important caveats about correlation, market-wide events, low-volume stocks, and news pre-pricing. This ensures the agent understands the tool's behavior and limitations.

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 lengthy but well-structured with sections (Key Metrics, Impact Classification, Use Cases, etc.). It is front-loaded with the core purpose. Every section adds value, though some redundancy exists (e.g., 'Why It Matters' partly repeats 'Analysis Insights'). Overall, it earns its length for a complex tool.

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 complexity and lack of an output schema, the description thoroughly covers what the tool returns: individual news impacts, aggregate statistics, top impacts, correlation strength, and event analysis. It also addresses use cases and limitations, making it contextually complete for agent invocation.

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%, with clear descriptions for all three parameters. The description adds value beyond the schema by explaining trade-offs for days_back and news_limit (e.g., 'Longer periods provide more data but may include less relevant events'). This enhances semantic understanding.

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 analyzes how news events affect stock prices over time, correlating news with price movements. It distinguishes itself from sibling tools like get_market_news and compare_peer_companies by focusing on impact analysis rather than just retrieving news or comparing peers.

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 explicit use cases with example queries, such as 'How does news affect Tesla stock price?' and 'Which news had the biggest impact on AAPL?'. It includes 'Important Notes' that caution about correlation vs. causation and other limitations, but lacks direct comparison to alternatives like get_market_news for when to use each.

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