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

Annotations only cover the safety profile (readOnly/idempotent/non-destructive); the description adds real behavioral context on top: data is citation-gated ('the KG refuses uncited data'), each row carries a citation URL, price data is only present 'when the entity is tradable', and input names are fuzzy-matched. This is exactly the kind of beyond-schema context that earns credit.

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?

Dense but well front-loaded: the first clause names the resource and universe, then the return categories, then the usage directive. The parenthetical examples are lengthy, but each one disambiguates slug formatting or relation types rather than 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 carries the full burden of describing returns and does so thoroughly across all three data categories, plus a caveat on when the price block appears. Input handling, return shape, and intended usage are all covered; nothing an agent needs to call it correctly is missing.

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?

The schema already documents the single 'entity' parameter at 100% coverage, so the baseline is 3. The description goes slightly beyond by disclosing that plain company names are 'fuzzy-matched' and by listing concrete slug examples (samsung-electronics, sk-hynix, tsmc), which tells the agent it need not produce an exact slug.

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 ('NightWatch Knowledge Graph lookup for a COMPANY/entity') and then enumerates exactly what comes back: sourced numeric fundamentals, typed relations (supplies/competes/customer_of/licenses), and a live HyperLiquid price block. No sibling tool in the list (get_token_intel, get_token_research, get_price) overlaps this company-fundamentals scope, and the description makes that boundary obvious.

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?

Explicitly says when to reach for it: 'Use this BEFORE reasoning about a company's fundamentals, competitors, supply chain, or a hedge on its equity perp.' That is strong, concrete context. It stops short of naming an alternative tool or stating a when-not condition, so it falls just short of a 5.

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