Read LinkedIn Conversation

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

The description discloses a non-obvious side effect: 'Opening a conversation may cause LinkedIn to mark it seen,' which aligns with readOnlyHint=false. It also promises explicit history completeness and truncation evidence, adding behavioral context beyond the annotations, which already note idempotency and non-destructiveness.

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?

Three dense sentences with no filler; the core read action and acceptable identifiers are front-loaded. The term 'reverse-virtualized' is jargon but purposeful, and each sentence adds real value: what it does, what it returns, and a side-effect warning.

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 covers identifiers, return evidence, and the seen-marking side effect. Since an output schema exists, return-value details are not required. Remaining gaps include the nature of required context_id/request_id and guidance on how to choose among the three lookup routes, preventing a perfect score.

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%, so the description must compensate. It clarifies profile_slug, conversation_id, and conversation_ref, but leaves context_id, request_id, and max_messages unexplained. max_messages is self-evident from its name, but context_id and request_id remain opaque, limiting the compensation.

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 explicitly states it reads LinkedIn conversation history, enumerating content types (messages, attachments, replies, edits, and reaction summaries) and clearly distinguishes itself from siblings by focusing on a single conversation. It also references messaging.search as the source of conversation_refs, differentiating the tool's role in the messaging workflow.

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?

It explains how to target a conversation (profile slug, visible conversation ID, or conversation_ref from messaging.search), which gives practical selection context. It doesn't explicitly say when not to use this tool, but the phrase 'conversation_ref returned by messaging.search' implies a search-then-read workflow that helps route usage.

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