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

Annotations already declare readOnly, idempotent and non-destructive; the description adds real context beyond them: results are precomputed from the monthly refresh, each result carries a title, verdict, 3-7 headline numbers with their data month, and change-log entries. It does not cover pagination or ranking behavior, keeping it short of a 5.

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?

Purpose is front-loaded and each sentence carries information, though the long parenthetical enumeration of the five page sets is dense. Effectively sized for the breadth of the corpus, with no filler sentences.

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?

With no output schema, the description compensates by specifying the return shape (title, verdict, headline numbers with data month, change log). Combined with clear query/slug behavior and sibling routing, an agent has everything needed to call it correctly.

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 baseline is 3; the description goes further by explaining that `query` accepts free words, a ticker or a portfolio name, and that `slug` returns one page in full while `query` is ignored, adding precedence semantics not visible in the schema alone.

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?

States a specific verb+resource (search MarketHeist's 90 published pages and return each page's verdict, numbers and citeable URL), and enumerates the content sets so the agent knows exactly what corpus it queries. Explicitly distinguishes itself from analyze_portfolio and run_backtest.

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?

Gives concrete when-to-use triggers ('does the 200-day average work on SPY', 'would a trend filter have helped All Weather in 2022') and an explicit when-not-to-use routing to analyze_portfolio/run_backtest for live numbers on a custom allocation.

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