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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Search Conversations

search_conversations

Search LinkedIn messages by keyword to find and filter relevant conversations. Returns up to 50 matching results for quick review.

Instructions

Search messages by keyword.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum 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.
keywordsYesSearch keywords to filter conversations

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

The description discloses no behavioral traits beyond the openWorldHint annotation. The schema's limit parameter warns that enumerating rows may mark them as read, but the description omits this side effect, and no destructiveHint/readOnlyHint is present to compensate.

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?

A single front-loaded sentence with no filler or redundancy. It is appropriately concise for a simple search tool.

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?

Given the low complexity, 100% schema coverage, and presence of an output schema, the one-sentence description is minimally viable. It could be more complete by mentioning the read side effect or search scope, but the schema already covers parameter-level details.

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%, with limit's side effects and defaults well documented. The description adds only 'by keyword', which aligns with the keywords parameter, but no extra meaning beyond the schema is needed or provided.

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

Purpose4/5

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

Description uses a specific verb ('Search') and resource ('messages'), which distinguishes it from sibling tools like get_inbox or get_conversation. However, it doesn't explicitly mention 'conversations' as the output unit or contrast with search_posts, leaving slight ambiguity about scope.

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

Usage Guidelines3/5

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

No explicit when-to-use or alternative guidance is provided. The name and sibling context imply it is the keyword search tool for messages/conversations, but the description does not say when to prefer it over get_inbox or get_conversation.

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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