LinkedIn MCP Server

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Search Conversations

search_conversations

Search LinkedIn messages by keyword to find specific conversations; set a low result limit to avoid marking many threads as read.

Instructions

Search messages by keyword.

Click-derived references require an observed different thread path after each row click. The first unverifiable click stops further row clicks and is reported in section_errors.search_results. Already-read text and independently extracted anchors retain their normal handling. A result without that diagnostic does not guarantee that every conversation was enumerated.

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": {
      "keywords": "<keywords>"
    },
    "name": "search_conversations"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of search-result rows to enumerate as conversation references (1-50, default 20). Each enumeration selects the row in LinkedIn's UI and may mark it as read, so a low cap is preferable for noisy queries.
`keywords`	Yes	Search keywords to filter conversations

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv4.26.0Sep 28, 2026

TDQS

C2.7/5.0

Behavior3/5

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

Annotations supply only openWorldHint=true, so the description must carry the behavioral load, and it does disclose two real traits: row clicks may mark conversations read, and an unverifiable click halts further enumeration with diagnostics in section_errors.search_results. However, this is expressed in opaque internal jargon ('click-derived references', 'observed different thread path') that an agent cannot readily act on, and it omits permissions or rate-limit context.

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

Conciseness2/5

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

The first sentence is properly front-loaded, but the second paragraph is long, jargon-heavy, and largely about the scraper's internals rather than what the caller needs. Those sentences do not earn their place for an agent selecting or invoking the tool.

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

Completeness3/5

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

An output schema exists, so return values need not be explained, and the description correctly gestures at where errors surface. Still, it leaves the agent without a clear picture of result shape, ordering, or how this tool relates to get_inbox/get_conversation, which is a meaningful gap for a search tool with a truncation caveat.

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%, and both parameters are already well documented in the schema, including the read-marking side effect of the limit parameter. The description adds no additional syntax, format, or matching semantics (e.g., how keywords are combined) beyond what the schema provides, so the baseline 3 applies.

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

Purpose3/5

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

The opening sentence gives a verb and resource ('Search messages by keyword'), but it names 'messages' while the tool is 'search_conversations', creating a small mismatch. It never distinguishes this tool from the closely related get_inbox and get_conversation siblings, and the rest of the description drifts into internal scraping mechanics rather than clarifying purpose.

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?

There is no explicit when-to-use guidance and no named alternative, despite three plausible siblings (get_inbox, get_conversation, search_posts). The only quasi-guidance is the schema note about preferring a low cap for noisy queries, which lives in the schema, not the description.

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