get_article

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

With no annotations provided, the description carries full burden but only partially discloses behavior. It mentions the API source ('Professional Network Data API') and return data structure, but doesn't address critical aspects like authentication requirements, rate limits, error conditions, or whether it accesses public vs. private data. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 efficiently structured with a clear purpose statement, API context, and separate Args/Returns sections. Every sentence adds value without redundancy, and information is front-loaded with the core functionality stated first.

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?

Given the tool's moderate complexity (single parameter, read operation), the description covers purpose, parameter meaning, and return content adequately. The existence of an output schema reduces the need to detail return values. However, the lack of behavioral context (authentication, errors, limits) prevents a perfect score despite good coverage elsewhere.

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 description coverage is 0%, but the description compensates well by clearly explaining the single parameter's purpose ('Full URL of the LinkedIn article') in the Args section. It adds essential context beyond the bare schema type, though it doesn't specify URL format requirements or validation rules.

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 clearly states the specific action ('Get the full content'), target resource ('LinkedIn article'), and scope ('complete article content, author information, and engagement metrics'). It distinguishes this read operation from sibling tools that analyze, create, or modify content rather than fetching raw article data.

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?

The description implies usage context by specifying it fetches 'full content' from a URL, suggesting it's for retrieving existing articles rather than creating or analyzing them. However, it doesn't explicitly state when to use this versus alternatives like 'get_profile_articles' or 'analyze_content_performance', nor does it mention prerequisites or exclusions.

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