Linked API MCP

get_conversation

Retrieve a LinkedIn conversation with a person by their profile URL, optionally filtering messages from a specific date.

Instructions

Allows you to get a conversation with a LinkedIn person using standard LinkedIn messaging.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "personUrl": "<personUrl>"
    },
    "name": "get_conversation"
  }
}

Input Schema

Name	Required	Description	Default
`personUrl`	Yes	The LinkedIn URL of the person whose conversation you want to poll (e.g., 'https://www.linkedin.com/in/john-doe')
`since`	No	Optional ISO 8601 timestamp to only retrieve messages since this date (e.g., '2024-01-15T10:30:00Z'). If not provided, the entire conversation history will be returned.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.0Jan 3, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description should fully disclose behavior. It only says 'get a conversation' implying read-only, but does not mention side effects (e.g., marking messages as read), authentication needs, or rate limits. The description is too sparse.

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?

The description is a single concise sentence that is easy to parse, no wasted words. Could be slightly improved by adding more details without losing conciseness.

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

Completeness2/5

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

Given no output schema and no annotations, the description lacks information on return format, error cases, or pagination. For a data retrieval tool, more depth is needed.

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 description coverage is 100% – both parameters are well-described in the schema. The description adds no additional meaning beyond the schema, so baseline score is appropriate.

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?

The description clearly states the action ('get a conversation') and the specific resource ('with a LinkedIn person'), distinguishing it from sibling tools like send_message or check_connection_status.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs alternatives such as nv_get_conversation or in what context (e.g., must have existing conversation, requires connection). The description lacks usage context and no exclusions are mentioned.

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