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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral detail beyond annotations: the specific fields returned, the history backfill state, and the explicit 'Returns voice: null when no profile has been extracted yet' behavior. This gives an agent a concrete expectation of the response even without an output schema.

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 a single, well-organized sentence that front-loads the core purpose, lists the concrete return contents, and ends with the important null-case edge behavior. Every clause earns its place and there is no redundant or filler language.

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?

This is a simple two-parameter read operation with complete schema coverage and safety annotations. The description sufficiently enumerates return fields and the null case, which is especially important because there is no output schema. It could be slightly more detailed about the shape of 'corpus stats' or 'structured profile', but the overall information is adequate for an agent to call the tool and interpret the result.

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 100%, with both linkedinAccountId and workspaceId already documented in the input schema. The description adds no parameter-level meaning beyond reinforcing that the profile belongs to a LinkedIn account, which is already reflected in the parameter names and schema descriptions. Baseline 3 applies because the schema carries the parameter documentation burden.

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: 'Get a LinkedIn account's conversation voice profile.' It enumerates the exact contents returned (status, version, style summary, corpus stats, structured profile, backfill state), which clearly distinguishes it from sibling tools like campaignstack_extract_conversation_voice and campaignstack_update_conversation_voice. The account-specific scope also separates it from campaignstack_get_workspace_voice.

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 this is the read tool for retrieving an existing conversation voice profile, and the null-return note suggests extraction may be needed when no profile exists. However, it does not explicitly name alternatives or state when to prefer extract_conversation_voice or update_conversation_voice. Usage context is clear but exclusions are left to inference.

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