analyze_portfolio

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

With no annotations, the description carries full behavioral disclosure. It discloses the AI backend (Groq with fallback) and lists return fields, adding value. However, it does not mention potential latency, cost, or error handling for invalid symbols, 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 extremely concise: three short sentences covering purpose, returns, mixing guidance, and AI model. Every sentence is necessary and adds unique information with no redundancy.

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 no output schema, the description adequately explains return values. It covers the input requirement (list of symbols) and usage context (mix US/Indian stocks). It lacks explicit error conditions or validation notes, but for a simple tool it is sufficiently complete.

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 baseline is 3. The description adds value by noting that US and Indian stocks can be mixed freely, which goes beyond the schema's example list. It also clarifies the overall purpose and return types.

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 performs an AI portfolio health check for a list of stocks, specifying the outputs (sentiment, diversification, risk, etc.). This distinguishes it from siblings like analyze_earnings (single-stock earnings) or compare_stocks (comparison), as it covers a holistic portfolio view.

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 when needing a portfolio-level analysis and explicitly allows mixing US and Indian stocks. However, it does not provide explicit guidance on when to avoid this tool or compare it directly to alternatives like analyze_sentiment or get_stock_insight.

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