LinkedIn Intelligence MCP Server

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

With no annotations provided, the description carries the full burden. It discloses the action (marking as seen/read) and warns about using an unofficial API, which is valuable context. However, it doesn't mention authentication requirements, rate limits, side effects (e.g., whether this affects notifications), or error conditions. The description adds some behavioral context but leaves significant gaps for a mutation tool.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured clearly, and the WARNING is placed effectively. There's minimal waste, though the 'Returns success status' could be slightly more informative.

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 mutation), no annotations, and the presence of an output schema (which handles return values), the description is partially complete. It covers the purpose and parameter well but lacks behavioral details like permissions, side effects, or error handling. The output schema existence means the description doesn't need to explain return values, but other gaps remain.

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 explicitly documents the single parameter 'conversation_urn' and clarifies it's a 'Conversation URN ID', adding meaningful semantics beyond the schema's type information. This fully compensates for the lack of schema descriptions, making the parameter well-understood.

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 verb ('mark') and resource ('conversation') with the specific action 'as read/seen'. It distinguishes this from sibling tools like 'get_conversation' or 'get_conversations' which are read-only operations, but doesn't explicitly differentiate from other potential conversation-modification tools (though none are present in the sibling list). The purpose is specific but could be slightly more distinctive.

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 provides no guidance on when to use this tool versus alternatives. While it's the only conversation-marking tool among siblings, it doesn't mention prerequisites (e.g., needing an existing conversation), when not to use it, or what 'success status' entails. The WARNING about unofficial API is useful but doesn't constitute usage guidance.

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