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

Annotations already declare readOnly/idempotent/closed-world, and the description goes well beyond them: coverage universe (~1,250 US-listed companies), source documents (10-K, 20-F since 2013, updated nightly), a crucial null-semantics caveat that a missing link is not proof no link exists, and an explicit statement that the links are descriptive risk data rather than predictive signals. That is exactly the kind of expectation-setting an agent cannot get from structured fields.

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?

Well front-loaded: what it returns, then when to use it, then coverage and caveats. The third paragraph's note about Zyberno's backtests is mildly promotional but still carries behavioral value by forestalling misuse as a signal. Slightly long, but nearly every sentence does work.

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 still fully specifies the return shape (grades, years, disclosed shares, source sentences with EDGAR links, aggregate dependency metrics, discontinued links). Coverage limits, freshness, and interpretation caveats are all present, so an agent has everything needed to call it and read the result 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?

Only one parameter and schema description coverage is 100%, so the schema already explains the ticker format, case insensitivity, and example values. The description adds nothing parameter-specific beyond reinforcing the 'US-listed company' scope, so the baseline of 3 applies.

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 opening sentence gives a specific verb (returns) and a precisely bounded resource (a company's SEC-reported supply chain), then enumerates the constituent parts: named suppliers, customers that name it, link grades, first-reported year, disclosed shares, and cited filing sentences. An agent can distinguish this from valuation or screening siblings 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?

Explicit positive triggers are listed ('who supplies a company, who its customers are, which companies depend on it, how concentrated... what is exposed if it stops supplying') and explicit exclusions name the alternatives: get_stock_valuation for valuation and screen_stocks for cross-company search. Both when-to-use and when-not-to-use are covered.

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