get_recommendations

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

With no annotations, the description carries the full burden. It discloses the returned data (analyst consensus and a 4-month trend) and the output format control (default text, json for structured data). It does not explicitly state read-only safety, rate limits, or data freshness, but for a simple read endpoint this is strong coverage.

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?

Three short sentences, front-loaded with what the tool returns, followed by usage then format. No filler or repetition. Efficient and easy to scan.

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 simple two-parameter read tool with no output schema and no annotations, the description provides the necessary return content, usage context, and format control. Missing only minor details like data freshness or symbol limits, which are not critical for correct 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%, so the schema already documents both parameters and the format enum. The description adds minor value by clarifying that json yields structured data and that text is the default, but this largely restates the schema's default note. 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 names a specific verb ('Returns') and resource ('analyst consensus and 4-month recommendation trend'), and adds a use case ('sentiment analysis'). It does not explicitly differentiate from sibling tools like get_stock_summary or get_key_stats, which could overlap in analyst data, so it stops short of a 5.

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?

It provides an implied usage context ('Use for sentiment analysis on a stock'), but offers no explicit when-to-use criteria, when-not-to-use conditions, or named alternatives among the many sibling tools. This is adequate but incomplete guidance.

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