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star_conversation

Idempotent

Star or unstar a LinkedIn conversation. Set star=false to remove the star. conversation_linkedin_id is the conversation_id returned by list_conversations. Housekeeping in the classic inbox; reversible (star=false). No quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
starNoTrue to star, False to unstar.
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
idempotency_keyNoOptional. A key you choose (a UUID is fine) that names this exact call. If you retry with the same key and the same arguments, the first call's result is returned and nothing is done twice on LinkedIn. Reusing a key with different arguments is refused. Keys expire after 24 hours.
conversation_linkedin_idYesConversation (thread) id, as returned by list_conversations.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
starredNoCurrent starred state

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover the safety profile (destructiveHint=false, idempotentHint=true, readOnlyHint=false), and the description adds genuinely non-annotation context: the action is reversible via star=false and consumes no quota. It does not, however, discuss any permission or scope requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three terse clauses front-load the core action, then the toggle, then provenance and constraints. No sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values need no explanation, and the annotations plus description together cover safety, idempotency, reversibility, and quota. The only minor gap is the absence of permission/scope guidance for a write operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so all four parameters including star, account_id, and idempotency_key are already documented in the schema. The description repeats the star=false semantics and the list_conversations origin of conversation_linkedin_id, adding little beyond the structured fields, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb pair (star/unstar) and the exact resource (a LinkedIn conversation), and even clarifies the toggle direction with star=false. An agent can immediately distinguish this from sibling tools like archive_conversation or delete_conversation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear usage context ("Housekeeping in the classic inbox") that frames when this tool is appropriate, and explains the unstar path. It stops short of naming alternatives such as archive_conversation or delete_conversation for when the intent is removal rather than marking.

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

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