Mark a LinkedIn conversation as read (by chat_id). Useful after the AI has processed/answered a thread so it does not re-surface as unread.
| Name | Required | Description | Default |
|---|---|---|---|
| chat_id | Yes | Chat ID from list_inbox_chats | |
| profile_id | No | UUID of the user_profile. Optional — defaults to active profile. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds minimal behavioral context beyond stating it marks as read, which is already implied. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences. First sentence states purpose and key identifier. Second provides usage motivation. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple idempotent mutation with good annotations and a clear single purpose, the description is complete. No output schema needed for such a tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters with descriptions (chat_id source, profile_id optional). Description adds no further parameter-level details. With 100% schema coverage, baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('mark as read') on a specific resource ('LinkedIn conversation') by a clear identifier ('chat_id'). It differentiates from sibling tools like reply_to_chat or get_chat_messages by focusing on the read-status operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly suggests usage after processing a thread to prevent re-surfacing as unread. This provides clear context for when to invoke, though it does not list explicit alternatives or when-not-to-use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but there is some overlap among search tools (search_job_postings, search_google_xray, search_linkedin_people, search_web) and messaging tools (send_connection_request, send_linkedin_message, reply_to_chat). However, detailed descriptions clarify the differences.
All tools follow a consistent verb_noun snake_case pattern (e.g., add_contacts_to_campaign, list_campaigns). No mixing of conventions.
48 tools is high but justifiable given the broad domain (LinkedIn outreach, CRM, campaigns, job postings, etc.). However, some tools could be consolidated (e.g., multiple search tools).
The tool set covers the entire workflow: searching, connecting, messaging, campaign management, CRM operations (fields, stages, tasks, notes), job postings, and posting. No obvious gaps.
