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bartest5

MCP Server for LinkedIn

by bartest5

Search Conversations

search_conversations
Read-only

Find LinkedIn conversations by keyword. Enter keywords to search messages, with an optional limit to control the number of results shown.

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

Behavior1/5

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

The main description is minimal, and annotations declare readOnlyHint=true, suggesting a safe read-only operation. However, the limit parameter's schema description states that each enumeration 'may mark it as read', which is a side effect that contradicts the readOnlyHint annotation. This is a serious inconsistency.

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 short sentence with no redundant wording. It is efficiently concise, though it could be slightly more informative without becoming verbose.

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?

The description is adequate for a simple search tool, and the schema plus output schema fill in some details. However, it lacks usage guidance and does not mention what the returned conversation references represent, leaving the overall picture incomplete.

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?

The input schema provides full descriptions for both parameters, including range, default, and a behavioral warning for limit. The main description adds no additional parameter meaning, so the baseline score of 3 applies given 100% schema coverage.

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?

The description states a clear action ('Search') and resource ('messages'), which aligns with the tool name. It does not explicitly distinguish from sibling search tools, but the resource is obvious enough.

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 guidance is given about when to use this tool versus alternatives like get_conversation or search_people. The usage is implied by the name and description, but no when-to-use or when-not-to-use context is provided.

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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