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LinkMCP: hosted LinkedIn MCP server

Mark Conversation Read

linkedin_mark_conversation_read
Idempotent

Mark a LinkedIn conversation as read, clearing its unread badge on LinkedIn. Reading messages via linkedin_get_conversation_messages does not clear it. Use linkedin_list_conversations to get the chat_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_idYesThe conversation ID to mark as read.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false and idempotentHint=true, so safety is covered structurally. The description adds real value beyond that by naming the external side effect (the unread badge on LinkedIn) and clarifying the read-vs-mark distinction. It stops short of mentioning rate limits or whether re-marking an already-read chat is a no-op (implied by idempotentHint only).

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 short sentences, front-loaded with the action and effect, then the two most likely agent errors (assuming a read clears it, and where to find chat_id). No filler.

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

Completeness5/5

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

For a single-parameter mutation with no output schema, the description covers action, side effect, the key misconception to avoid, and the source of the required ID. An agent can invoke this correctly with no further information.

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

Parameters4/5

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

Schema coverage is 100% for the single chat_id parameter, so the baseline is 3. The description adds provenance the schema does not: it tells the agent to obtain chat_id from linkedin_list_conversations, which is actionable information beyond the field's type description.

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+resource ('Mark a LinkedIn conversation as read') plus the observable effect ('clearing its unread badge'). This cleanly separates it from the adjacent read-only sibling linkedin_get_conversation_messages, which an agent could otherwise confuse with it.

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

Usage Guidelines5/5

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

Gives explicit when-to-use context: reading messages via linkedin_get_conversation_messages does NOT clear unread state, so this tool is required for that outcome. It also names linkedin_list_conversations as the source of the chat_id, so the agent knows the prerequisite call.

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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