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MeMCP – MCP for Personal Profile

by y42·Feb 21, 2026·3 points·0 comments
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AI Analysis

●MidShip It

Personal profile MCP with scrapers, but the connectors are limited and LLM agent use cases are early.

Strengths
  • •Multi-source aggregation (GitHub, LinkedIn, RSS, Medium, YAML) is thorough for professional data.
  • •Skill/proficiency metrics based on recency, duration, frequency show thoughtful scoring.
  • •Flexible scraper framework allows custom data sources beyond built-in connectors.
Weaknesses
  • •Only implemented connectors are the ones the author uses; generalization unclear and limited.
  • •MCP adoption is still early; unclear if agents actually need this vs. inline context injection.
Target Audience

Professionals using MCP-compatible AI agents for CV generation, profile queries, skill analysis

Similar To

JSON Resume · Sourced.pro · Manual LinkedIn APIs

Post Description

I know someone built something similar years ago.

However, this is a small side project that kind of escalated: meMCP. It’s a "personal profile protocol," which means it holds data about your professional milestones, education, side projects, and whatever else you feed it. It consists of two parts:

> Backend with scrapers, crawlers, and processors to read data from different sources—primarily LinkedIn for professional history, but also RSS feeds or even Medium (provided you export the raw DOM from your stories overview). Right now, the "connectors" are somewhat limited, as I’ve focused on platforms I actually use. Everything you input is classified into skills, technologies, and general tags. A few metrics are then calculated to score proficiency or the relevance of your capabilities.

> Frontend for your favorite LLM/Agent. It contains everything needed to allow any agent to interact with this MCP. A use case would be generating a CV for job applications, drafting cover letters, or simply showcasing your achievements.

See it in action:

https://mcp.nickyreinert.de/?lang=en

or

https://mcp.nickyreinert.de/human

Tell me what you think.

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