extract_entities

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

With no annotations, the description fully carries the burden. It discloses read-only behavior for URL input, zero network for text input, determinism, rule-based nature, and output format (list with type, confidence, sameAs URIs). No contradictions.

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?

The description is concise (~100 words), front-loaded with the main purpose, then covers behavior, usage, and alternatives. Every sentence adds value with no redundancy.

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?

The description explains purpose, behavior, when to use, and output format. It lacks explicit handling of mutual exclusivity (e.g., what if both `url` and `text` are provided) and error cases, but overall it is sufficiently clear for a retrieval tool with three parameters.

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?

Input schema covers 100% of parameters with descriptions. The description adds no new parameter-level meaning beyond the schema; it merely restates that 'url' or 'text' must be provided. Baseline 3 is appropriate given full schema coverage.

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 states a specific verb and resource: 'Extract named entities, linked concepts, and sameAs graph nodes from a page's content and structured data.' It clearly distinguishes itself from sibling tool `audit_schema` by noting that to validate JSON-LD, that tool should be used instead.

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 states when to use ('building an entity map for schema generation, or auditing whether a page's entities match its target topic') and when not to use ('To validate the JSON-LD itself, use `audit_schema`'). Provides clear context and an alternative.

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