Ana-Catalina MCP Server

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Official Model Context Protocol (MCP) Server with Streamable HTTP transport over FastMCP, exposing an interactive CV and portfolio for AI assistants (Google Antigravity, Claude.ai, Cursor, Windsurf, Gemini) and LLM clients. Includes a self-contained web showcase and a production-ready container for Google Cloud Run.

Project Description

This project provides an official Model Context Protocol (MCP) server built in Python that enables AI assistants, hiring managers, and technical evaluators to interactively query the professional experience, technical skill matrix, featured projects, and job compatibility of Ana-Catalina Villalobos Contardo (Data Scientist & Machine Learning Engineer).

Key Features

• Interactive Web Showcase & Playground: Served at /demo (browsers hitting / are redirected to the product page) using the Pastel-Tech Design System. Allows human visitors and evaluators to test job fit and query the curriculum directly in the browser with deterministic accuracy and instant in-memory responses.

• Transparent HTTP Content Negotiation: Serves the interactive web interface to browsers (Accept: text/html) while preserving the structured JSON discovery payload for programmatic agents and curl.

• Modern Streamable HTTP Transport (/mcp): Native transport standard for direct cloud connections from Google Antigravity (mcp_config.json), Claude.ai (custom connectors), Gemini (gemini.com Connected Apps) and Cursor / Windsurf (mcp.json).

• 9 Dedicated MCP Tools: Granular exploration of work history, skill taxonomy by category/level, highlighted projects, automated job fit scoring, full-text curriculum search, education, contact details, and general profile.

• Zero-Latency In-Memory Architecture: Clean data validation using Pydantic v2 loaded into memory on container startup (<2ms response time).

Related MCP server: Bijon Portfolio MCP Server

Architecture

flowchart TD
    subgraph ClientLayer["AI Clients & Consumers"]
        AG["Google Antigravity (mcp_config.json)<br/>Streamable HTTP (POST /mcp)"]
        A["Claude.ai (Custom Connector)<br/>Streamable HTTP (POST /mcp)"]
        B["Cursor / Windsurf (mcp.json)<br/>Streamable HTTP (POST /mcp)"]
        G["Gemini (gemini.com Connected Apps)<br/>Streamable HTTP (POST /mcp)"]
    end

    subgraph CloudLayer["Google Cloud Run / Serverless Host"]
        C["FastMCP App (:8080)<br/>/mcp & Custom Routes"]
        D["9 Registered MCP Tools"]
        E["CV Service & Pydantic Engine<br/>(models/cv.py)"]
        F[("data/cv_data.json<br/>(In-Memory Dataset)")]
    end

    AG <-->|"JSON-RPC Streamable HTTP"| C
    A <-->|"JSON-RPC Streamable HTTP"| C
    B <-->|"JSON-RPC Streamable HTTP"| C
    G <-->|"JSON-RPC Streamable HTTP"| C
    C <--> D
    D <--> E
    E <--> F

MCP Tools Catalog

The server exposes 9 official tools registered via the Model Context Protocol:

Key Learnings

1. MCP Transport Evolution (Migration to Streamable HTTP):

   • Traditional remote MCP servers relied on complex multi-endpoint SSE combinations (/sse and /messages/).

   • The modern MCP specification introduced Streamable HTTP (/mcp) via a single unified HTTP POST endpoint supporting both JSON responses and streaming events (Accept: application/json, text/event-stream).

   • This eliminates local stdio bridge overhead for remote clients and enables direct cloud connections from Claude.ai, Cursor, and Windsurf.

2. Decoupled Architecture & Schema Validation:

   • Separating raw data (data/cv_data.json), interface contracts (models/cv.py), and domain services (services/cv_service.py) ensures that CV content updates never compromise server stability or type safety.

3. Pinned Stability Across MCP SDK Versions:

   • Version 2.0 of the official MCP SDK introduced breaking renames to FastMCP. Pinning mcp>=1.3.0,<2 preserves Cloud Run production stability without forced premature rewrites.

4. Automated Upstream Drift Detection:

   • Built a GitHub Actions workflow (upstream-drift.yml) using semantic content comparison to detect discrepancies and missing keys between data/cv_data.json and sibling repositories (anacatalina-cv, projects-hub).

Local Setup & Development

1. Clone Repository & Setup Virtual Environment

git clone https://github.com/AnaCataVC/anacatalina-mcp.git
cd anacatalina-mcp

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows PowerShell: .venv\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

2. Run Test Suite

pytest tests/ -v

3. Start Local MCP Server

uvicorn server:app --host 0.0.0.0 --port 8080 --reload

Connecting AI Assistants

• Google Antigravity / Gemini CLI (~/.gemini/config/mcp_config.json):

  {
    "mcpServers": {
      "anacatalina-cv": {
        "url": "https://mcp.ana-catalina.com/mcp"
      }
    }
  }

• Claude.ai: Settings → Connectors → Add custom connector → URL: https://mcp.ana-catalina.com/mcp

• Cursor / Windsurf (mcp.json):

  {
    "mcpServers": {
      "anacatalina-cv": {
        "url": "https://mcp.ana-catalina.com/mcp"
      }
    }
  }

License

This project is licensed under the MIT License. See LICENSE for details.