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LinkMCP: hosted LinkedIn MCP server

Get Conversation Messages

linkedin_get_conversation_messages
Read-onlyIdempotent

Get messages from a LinkedIn DM conversation, newest first. Returns message text, timestamp, sender ID, read status, and attachments. Each message includes the sender's name, and their headline and profile link when LinkedIn provides them. Use linkedin_list_conversations first to get the chat_id. One call returns one page of at most limit messages (default 50, max 250). When has_more is true, older messages exist and the oldest message returned is NOT the start of the conversation: call again with the same chat_id and cursor set to next_cursor, and repeat until has_more is false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of messages to return per page (1–250, default 50).
cursorNoPagination cursor: pass next_cursor from the previous response to get the next page of older messages. Omit for the first page (the most recent messages).
chat_idYesThe conversation/chat ID to fetch messages from.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered; the description goes further by disclosing the payload (text, timestamp, sender ID, read status, attachments, sender name/headline/profile link) and, critically, the pagination invariant that a returned page is not necessarily the start of the conversation. That warning is behavioral context not derivable from the schema.

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

Conciseness4/5

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

Front-loaded with purpose and return contents, then prerequisites, then pagination. Slightly redundant in restating limit's default 50 / max 250 that the schema already carries, but every sentence otherwise earns its place.

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?

No output schema exists, so the description carries the return-value burden and does so thoroughly, including field-level contents and the has_more/next_cursor pagination contract. Nothing an agent needs to call this correctly and iterate pages is missing.

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%, so the schema documents limit and cursor adequately and the baseline is 3. The description adds cross-call meaning to cursor by tying it to next_cursor and has_more across repeated calls, which the schema's per-parameter text does not fully convey.

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 (Get) and resource (messages from a LinkedIn DM conversation) and immediately distinguishes itself from linkedin_list_conversations by naming that tool as the prerequisite for obtaining chat_id. Ordering ('newest first') is front-loaded so the agent knows the shape of the result.

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?

Explicitly routes the agent: 'Use linkedin_list_conversations first to get the chat_id,' and prescribes the exact pagination loop (call again with same chat_id and cursor=next_cursor, repeat until has_more is false). Both the entry condition and the continuation condition are stated, leaving nothing to inference.

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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