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

Goes well past the annotations (which already declare readOnly/idempotent/non-destructive): it discloses the ~3KB default payload size, that include sections are fetched via parallel API calls (a latency implication), that peers include a median row and pipeline-included self row flagged `is_self`, and critically that a `null` metric means 'not published' and must never be read as 0. That null-semantics warning prevents a real correctness error.

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?

Front-loaded with base payload, then a clean bullet list for the three opt-in sections, then a copy-pasteable example. The peers bullet is long, but its density is justified by the column list and the null/`is_self` semantics it carries; little here is padding.

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 does the full job of describing return values — base fields plus each optional section — and even documents data-quality edge cases. An agent has enough to choose parameters and interpret the response without guessing.

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 the alias parameters (symbol/ticker/identifier) are already documented, but the description adds meaning the schema does not: what each include value actually returns (the ~20 technical indicators by name, the buy/hold/sell consensus, the peers column set and median). It does not cover the three alias names, which remain schema-only.

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 and resource ('Get detailed company overview') and then enumerates exactly what the base payload contains — sector, about, pros/cons, fundamentals, growth, quote. An agent can distinguish this from get_financials, get_stock_quote, or get_live_quote without opening any schema.

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 a concrete condition for each optional section ('include ["technicals","ratings"] for a full analysis view') and frames include as an opt-in that costs parallel API calls. It stops short of naming sibling alternatives (e.g. get_financials for statements, get_stock_quote for a bare price), so the when-not-to-use side is left implicit.

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