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

Beyond the annotations (readOnlyHint=false, destructiveHint=true), the description adds the critical behavioral safeguard that the user must have clearly asked or approved the final text. This is a meaningful behavioral guideline not present in structured data, ensuring the agent understands this is a real-world, consent-required action.

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 sentences, each with a distinct purpose: core action, prerequisite, and approval condition. The description is front-loaded with the primary action and contains no filler, making it quick for an agent to parse.

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?

For a simple 3-parameter tool with annotations and no output schema, the description covers the essential context: what the tool does, how to obtain the required chat_id, and the approval requirement. It doesn't describe the return value, but that's not necessary given the absence of an output schema and the simplicity of the action.

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?

The description adds value by specifying that chat_id normally comes from linkedin_list_conversations, supplementing the already detailed schema description for chat_id. The phrase 'final text' also gives a slight hint about the text parameter's role. With 67% schema coverage, this partial compensation is adequate, though text itself could use more explicit semantics.

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 action ('Send a message') on a specific resource ('an EXISTING LinkedIn chat'), and the 'EXISTING' qualifier clearly distinguishes it from conversation-starting siblings like linkedin_start_conversation. It also names the prerequisite chat_id and its typical source, leaving no ambiguity about what this tool does.

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 an explicit when-to-use condition: 'Use only when the user has clearly asked to send/approved the final text.' It also instructs that chat_id is normally obtained from linkedin_list_conversations, giving a clear workflow. It does not explicitly name alternatives or exclusions, but the 'EXISTING' constraint implicitly steers away from new-chat tools.

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