Skip to main content
Glama

Search Conversations

search_conversations
Read-only

Search your LinkedIn messages for conversations containing specific keywords to quickly find relevant discussions.

Instructions

Search messages by keyword.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesSearch keywords to filter conversations
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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

The description does not add any behavioral context beyond what the annotations already provide (readOnlyHint, openWorldHint). It misses the opportunity to disclose that each result row may be marked as read, which is hinted in the schema but not in the tool description.

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

Conciseness3/5

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

The description is a single sentence, making it concise but overly terse. It could be slightly expanded to include usage context 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?

Despite having an output schema, the description is too minimal. It does not clarify the scope of search (e.g., user's conversations vs. public messages) or provide any guidance on expected output behavior, leaving ambiguity for an AI agent.

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 coverage is 100%, so the tool description is not required to add parameter details. However, it adds no extra meaning beyond the schema, resulting in a baseline score of 3.

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 clearly states the action ('Search messages') and the resource ('conversations'), using a specific verb and resource. It distinguishes from sibling tools like search_people or get_inbox, though it could be more precise about whether it searches messages within conversations or conversation titles.

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 is provided on when to use this tool versus alternatives like get_conversation or get_inbox. There is no mention of prerequisites, limitations, or comparison with other search tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/stickerdaniel/linkedin-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server