Claude MCP workflow automates LinkedIn lead re-engagement with adaptive constraints
A developer shared a workflow using Claude with MCP (Model Context Protocol) to automate re-engagement with old LinkedIn connections. Instead of manually reviewing profiles and writing messages, the system handles the entire process.
Workflow breakdown
The system performs four main functions:
- Identifies and prioritizes leads (categorized as hot/warm)
- Detects basic engagement signals
- Generates contextual re-engagement messages
- Executes the outreach step
Constraint handling and results
In a test with 7 targeted leads:
- 5 messages were successfully sent
- 2 were skipped due to LinkedIn restrictions (connection settings/limits)
Instead of forcing actions that would violate platform constraints, the system suggested a fallback approach: engage with the person's content first, then retry the outreach later. This adaptive behavior made the workflow feel less like rigid automation.
Manual process replacement
The workflow replaces the manual process of:
- Opening individual profiles
- Checking context for each connection
- Writing personalized messages one by one
The developer noted they're still iterating on the scoring logic and prioritization aspects of the system, and expressed interest in hearing about other MCP workflows beyond basic text generation.
📖 Read the full source: r/ClaudeAI
👀 See Also
A TDD Development Flow Using AI Agents for Website Projects
A developer shares their workflow for building websites using AI coding agents with TDD, detailing setup steps, iterative processes, and specific commands for running tests with local models like Qwen3.5-27B.
Claude Sonnet 4.6 Grades Bug Reports from Four Qwen3.5 Local Models
A developer tested four Qwen3.5 variants by having them generate bug reports for an iOS game issue, then had Claude Sonnet 4.6 grade the reports. The models correctly identified a Swift bug where equipment border colors don't reset, but test code had compilation issues.
Karis CLI Architecture: Using Claude for Planning, Not Execution
Karis CLI uses a three-layer architecture where Claude handles planning and reasoning while pure code executes tasks reliably, creating a stable agent setup that separates LLM capabilities from execution.
Building a Slack-based debugging system for non-technical Claude users
A developer created a local Claude skill that polls a Slack channel every 7 seconds, allowing non-technical team members to get debugging help by pinging their Claude instance directly in Slack threads.