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Female Wellness & Lifestyle MCP Server

by rootrasel

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Female Wellness & Lifestyle MCP Server

A production-oriented, modular, and evidence-grounded Model Context Protocol (MCP) server tailored for a Female Wellness & Lifestyle AI Platform.

Built with FastMCP / MCPServer, ChromaDB, Sentence Transformers, and the Anthropic Claude API, this server connects AI host applications (such as Claude Desktop or autonomous agents) to verified female health research, life-stage context, and deterministic clinical safety guardrails over standard I/O streams (stdio).

1. Project Overview

The platform addresses female health, hormonal rhythms, longevity, athletic periodization, and maternal wellness across six primary life-stage journeys:

                               Female Wellness AI Platform
                              Six Personalized Life-Stage Journeys
  ┌──────────────────────┬──────────────────────┬──────────────────────┐
  │  Beauty & Radiance   │  Cycle & Fertility   │ Athlete Performance  │
  ├──────────────────────┼──────────────────────┼──────────────────────┤
  │Pregnancy & Postpartum│    Perimenopause     │  Lifelong Thriving   │
  └──────────────────────┴──────────────────────┴──────────────────────┘

Beauty & Radiance: Connects internal health with external vitality. Emphasizes gut-skin axis health, epidermal barrier nutrition, cellular hydration, hair follicle health, and restorative sleep.
Cycle & Fertility: Establishes hormonal baselines and fertility awareness. Focuses on the four phases of the menstrual cycle, basal body temperature (BBT) tracking, ovulation awareness, fertile window education, and luteal phase support.
Athlete Performance: Optimizes training loads and athletic recovery around female physiology, Heart Rate Variability (HRV), autonomic nervous system tracking, cycle-based periodization, and RED-s prevention.
Pregnancy & Postpartum: Supports maternal wellness throughout pregnancy and Fourth Trimester recovery. Covers trimester-specific nutrition (choline, folate, iron, DHA), symptom education, and pelvic floor restoration.
Perimenopause: Navigates midlife hormonal transitions with clarity. Emphasizes vasomotor symptom thermoregulation (hot flashes, night sweats), sleep preservation, neurosteroid fluctuations, and bone mineral density preservation.
Lifelong Thriving: Supports healthy aging, functional mobility, sarcopenia prevention, cognitive neuroprotection, and long-term vitality.

IMPORTANT

These six categories representproduct journeys and wellness domains, not clinical medical diagnoses.

Related MCP server: Clarity MCP

2. System Architecture

The platform uses a dual-mode architecture operating over the Model Context Protocol:

Host-Orchestrated Reasoning (Primary MCP Mode): An MCP client (such as Claude Desktop) invokes retrieval tools (search_wellness_knowledge, get_wellness_recommendations), inspects resources (wellness://...), and performs its own language reasoning directly over the returned citations.
Server-Side Generation Mode: Headless clients or automated agents invoke ask_wellness_question, which executes end-to-end RAG retrieval, passes evidence to the internal Anthropic Claude service, and returns synthesized answers with citations.

Architectural Diagram

MCP Client (Claude Desktop / CLI Host)
                  │
                  │ STDIO (JSON-RPC 2.0)
                  ▼
┌─────────────────────────────────────────────────────────────┐
│                 Python FastMCP Server                       │
│                                                             │
│  ┌───────────────────────┐       ┌───────────────────────┐  │
│  │   Life Stage Engine   │       │  Safety & Escalation  │  │
│  │ (life_stage_service)  │       │   (safety_service)    │  │
│  └──────────┬────────────┘       └──────────┬────────────┘  │
│             │                               │               │
│  ┌──────────▼───────────────────────────────▼────────────┐  │
│  │                    RAG Pipeline                       │  │
│  │  Retriever ──► Sentence-Aware Chunker ──► Doc Loader  │  │
│  └──────────────────────────┬────────────────────────────┘  │
└─────────────────────────────┼───────────────────────────────┘
                              │
               ┌──────────────┴──────────────┐
               ▼                             ▼
     ┌───────────────────┐         ┌───────────────────┐
     │     ChromaDB      │         │   Anthropic API   │
     │   Vector Store    │         │ (Claude-3.5-Sonnet│
     │ (HNSW Cosine idx) │         │  Grounded Synth)  │
     └─────────┬─────────┘         └───────────────────┘
               │
               ▼
     ┌───────────────────┐
     │   Knowledge Base  │
     │  (PDF, DOCX, MD)  │
     └───────────────────┘

Critical Protocol Rule: STDIO Purity

FastMCP communicates with MCP clients using standard input and output (stdio). Any arbitrary print statement or third-party log written to stdout corrupts the JSON-RPC framing and disconnects the client. In this architecture:

sys.stdout is reserved exclusively for MCP JSON-RPC protocol messages.
All application logs, diagnostics, and vector database outputs are routed strictly to sys.stderr.
API keys (sk-ant-...) are intercepted and masked by a custom logging filter.

3. Technology Stack

Technology	Version / Spec	Purpose
Python	3.12+ (tested on 3.13.5)	Core application runtime
FastMCP (`mcp[cli]`)	`>=1.3.0` (mcp 2.x compatible)	MCP protocol server, tools, resources, and prompt registry
Anthropic Python SDK	`>=0.40.0`	Claude API integration for server-side grounded synthesis
ChromaDB	`>=0.5.0`	Embedded local vector database with persistent SQLite/Parquet backing
Sentence Transformers	`>=3.0.0`	Local dense vector embedding generation
Embedding Model	`BAAI/bge-small-en-v1.5`	384-dimensional dense embeddings running locally on CPU
PyMuPDF (`pymupdf`)	`>=1.24.0`	Page-accurate text extraction from PDF research papers
python-docx	`>=1.1.0`	Structural text extraction from DOCX documents
Pydantic v2 & Settings	`>=2.7.0`	Centralized settings, validation, and JSON schemas
Pytest & Pytest-Asyncio	`>=8.0.0`	Comprehensive unit and integration test suite
Ruff	`>=0.6.0`	High-speed linting and code formatting

4. Directory Structure

mcp-server/
├── .env.example                               # Environment variable template
├── .gitignore                                 # Git ignore (excludes chroma_db/, .venv, .env)
├── LICENSE                                    # MIT License
├── README.md                                  # Comprehensive documentation
├── pyproject.toml                             # PEP 621 package metadata & configuration
│
├── src/
│   └── female_wellness/
│       ├── __init__.py                        # Package root
│       ├── server.py                          # FastMCP entry point & STDIO transport runner
│       ├── config.py                          # Centralized Pydantic Settings
│       │
│       ├── core/                              # Core constants and infrastructure
│       │   ├── __init__.py
│       │   ├── constants.py                   # Life stage IDs, categories, system defaults
│       │   ├── exceptions.py                  # Domain exception hierarchy (FemaleWellnessError)
│       │   └── logging_config.py              # Stderr-only logging with secret key redaction
│       │
│       ├── mcp/                               # Model Context Protocol endpoints
│       │   ├── __init__.py
│       │   ├── tools/                         # 7 MCP Tools
│       │   │   ├── __init__.py
│       │   │   ├── life_stage_tools.py        # Journey discovery & feature inspection
│       │   │   ├── knowledge_tools.py         # Semantic search, sources, stats
│       │   │   └── recommendation_tools.py    # Evidence-grounded QA & recommendations
│       │   ├── resources/                     # 3 MCP Resources (wellness://...)
│       │   │   ├── __init__.py
│       │   │   └── knowledge_resources.py    # Static/dynamic context endpoints
│       │   └── prompts/                       # 3 MCP Prompt Templates
│       │       ├── __init__.py
│       │       └── wellness_prompts.py        # Question answering & recommendation prompts
│       │
│       ├── rag/                               # RAG & Vector Subsystem
│       │   ├── __init__.py
│       │   ├── embeddings.py                  # SentenceTransformer wrapper (bge-small-en-v1.5)
│       │   ├── vector_store.py                # BaseVectorStore abstraction & ChromaVectorStore
│       │   ├── retriever.py                   # Semantic search & threshold filter
│       │   ├── document_loader.py             # PDF, DOCX, TXT, MD loader with page tracking
│       │   ├── chunking.py                    # Sentence-aware chunker with overlap
│       │   └── pipeline.py                    # Ingestion coordinator & context assembler
│       │
│       ├── services/                          # Business logic layer
│       │   ├── __init__.py
│       │   ├── anthropic_service.py           # Claude API integration & error handling
│       │   ├── life_stage_service.py          # 6 Life-stage journey engine & privacy rules
│       │   └── safety_service.py              # Deterministic red-flag & boundary engine
│       │
│       ├── schemas/                           # Pydantic v2 schemas
│       │   ├── __init__.py
│       │   ├── documents.py                   # Document, chunk, and collection schemas
│       │   ├── recommendations.py             # Answer, recommendation, and item models
│       │   └── wellness.py                    # Citations, context, and user profile schemas
│       │
│       └── utils/                             # Utilities
│           ├── __init__.py
│           └── text_utils.py                  # Unicode normalization & sentence splitting
│
├── knowledge_base/
│   ├── raw/                                   # Source documents organized by journey
│   │   ├── beauty_radiance/
│   │   ├── cycle_fertility/
│   │   ├── athlete_performance/
│   │   ├── pregnancy_postpartum/
│   │   ├── perimenopause/
│   │   ├── lifelong_thriving/
│   │   └── general/
│   └── processed/                             # Ingestion artifacts & caches
│
├── chroma_db/                                 # Persistent ChromaDB storage (auto-created)
│
├── scripts/                                   # Operational CLI utilities
│   ├── ingest_documents.py                    # Batch document ingestion CLI (--dry-run supported)
│   ├── clear_vector_store.py                  # Vector store reset utility
│   └── inspect_collection.py                  # Collection inspection and record viewer
│
└── tests/                                     # Comprehensive test suite (74 tests)
    ├── __init__.py
    ├── test_config.py                         # Settings, logging, and exceptions
    ├── test_document_loader.py                # PDF, DOCX, TXT, MD loading & metadata
    ├── test_chunking.py                       # Sentence boundaries & overlap preservation
    ├── test_embeddings.py                     # Vector dimension, normalization, and ranking
    ├── test_vector_store.py                   # ChromaDB CRUD, persistence, filtering
    ├── test_retriever.py                      # Retrieval, threshold cutoffs, RAG pipeline
    ├── test_life_stages.py                    # 6 Journeys, validation, privacy non-inference
    ├── test_anthropic_service.py              # Claude generation & exception translation
    ├── test_mcp_tools.py                      # MCP tools, resources, and prompt execution
    └── test_safety.py                         # Medical red-flags & prescriptive boundaries

5. Installation & Setup

Prerequisites

Linux, macOS, or Windows (WSL recommended on Windows).
Python 3.12 or higher.

Step-by-Step Installation

# 1. Clone the repository
git clone <repo-url>
cd mcp-server

# 2. Create a virtual environment
python3 -m venv .venv

# 3. Activate the virtual environment
source .venv/bin/activate
# On Windows: .venv\Scripts\activate

# 4. Install the package in editable mode with development dependencies
pip install --upgrade pip
pip install -e ".[dev]"

6. Environment Configuration

Copy the provided .env.example file to create your local .env:

cp .env.example .env

Configurable Variables

Variable	Default Value	Description
`ANTHROPIC_API_KEY`	(Optional)	Anthropic Claude API key (`sk-ant-...`). Optional if Claude Desktop handles reasoning; required for server-side `ask_wellness_question`.
`ANTHROPIC_MODEL`	`claude-3-5-sonnet-20241022`	Claude model identifier for server-side generation.
`CHROMA_PERSIST_DIRECTORY`	`./chroma_db`	Local filesystem directory where ChromaDB stores SQLite and Parquet files.
`CHROMA_COLLECTION_NAME`	`female_wellness_knowledge`	ChromaDB collection name.
`EMBEDDING_MODEL`	`BAAI/bge-small-en-v1.5`	Sentence Transformers Hugging Face identifier or local checkpoint path.
`CHUNK_SIZE`	`800`	Target maximum character length for each document chunk.
`CHUNK_OVERLAP`	`150`	Character overlap between consecutive chunks.
`TOP_K`	`5`	Default number of chunks retrieved per search.
`SIMILARITY_THRESHOLD`	`0.35`	Minimum cosine similarity score ($1.0 - \text{distance}$) for evidence acceptance.
`LOG_LEVEL`	`INFO`	Logging level (`DEBUG`, `INFO`, `WARNING`, `ERROR`). Always written to stderr.

7. Knowledge Ingestion CLI

Documents are placed into knowledge_base/raw/ grouped by journey folder:

knowledge_base/raw/
├── beauty_radiance/
├── cycle_fertility/
├── athlete_performance/
├── pregnancy_postpartum/
├── perimenopause/
├── lifelong_thriving/
└── general/

Supported Document Formats

PDF (.pdf): Extracted page-by-page using PyMuPDF (pymupdf), tracking 1-based page numbers.
Word (.docx): Body paragraphs and tables extracted using python-docx.
Markdown (.md): Structured headers and sections extracted.
Plain Text (.txt): Text cleaned and normalized.

Running Ingestion

# 1. Dry run: Verify discovery, page counts, and chunk planning without modifying ChromaDB
python scripts/ingest_documents.py --dry-run

# 2. Live Ingestion: Chunk, embed, and index into ChromaDB
python scripts/ingest_documents.py

# 3. Inspect Collection: Verify indexed chunk counts and view sample records
python scripts/inspect_collection.py --limit 3

# 4. Clear Vector Store: Reset the collection if re-indexing from scratch
python scripts/clear_vector_store.py --force

8. MCP Tools, Resources, and Prompts Catalog

A. MCP Tools

Tool Name	Parameters	Description
`get_available_life_stages`	None	Returns all six supported female wellness journeys with summaries.
`get_life_stage_details`	`life_stage: str`	Returns comprehensive specifications, supported wellness categories, and features for a given journey.
`search_wellness_knowledge`	`query: str`, `life_stage: str?`, `category: str?`, `top_k: int = 5`	Semantic similarity search across indexed documents with metadata filtering. Returns text, source, page number, and similarity score.
`ask_wellness_question`	`question: str`, `life_stage: str`, `user_context: dict?`	Evaluates medical safety rules, retrieves relevant evidence, and generates an evidence-grounded answer with citations.
`get_wellness_recommendations`	`life_stage: str`, `category: str?`, `user_context: dict?`	Retrieves actionable, evidence-backed lifestyle, nutrition, and recovery recommendations for the selected journey.
`get_knowledge_sources`	`life_stage: str?`, `category: str?`	Catalogs indexed source documents, page counts, and coverage.
`get_knowledge_base_stats`	None	Returns aggregate statistics: total chunks, documents, available journeys, and categories.

B. MCP Resources

Resource URI	MIME Type	Description
`wellness://life-stages`	`application/json`	Complete JSON specifications for all six life-stage journeys.
`wellness://knowledge-base`	`application/json`	Real-time statistics, document counts, and categories indexed in ChromaDB.
`wellness://safety-guidelines`	`text/markdown`	Ethical guidelines, non-prescriptive boundaries, and red-flag escalation principles.

C. MCP Prompts

Prompt Name	Arguments	Description
`wellness_question_prompt`	`question: str`, `life_stage: str`	Guides the host LLM to formulate compassionate, evidence-grounded answers citing sources and acknowledging biological variation.
`life_stage_recommendation_prompt`	`life_stage: str`, `category: str = "general"`	Directs generation of phase-appropriate nutrition, sleep, and exercise guidance.
`evidence_based_wellness_prompt`	`topic: str`, `life_stage: str`	Instructs the model to synthesize physiological mechanisms and study findings.

9. Running the MCP Server

Standalone CLI Execution

# Run server using Python module
python -m female_wellness.server

# Or use the installed console entrypoint
female-wellness-mcp

The server will start listening on stdio for JSON-RPC 2.0 protocol messages. Diagnostic logs will appear on stderr.

10. Claude Desktop Integration

Claude Desktop communicates with MCP servers by spawning them as background subprocesses over stdio.

Locating Your Claude Desktop Configuration

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Configuration Snippet

Add the following configuration to claude_desktop_config.json. Use the absolute path to your virtual environment's Python executable and project directory:

{
  "mcpServers": {
    "female-wellness": {
      "command": "/home/rasel/Projects_hub/mcp-server/.venv/bin/python",
      "args": [
        "-m",
        "female_wellness.server"
      ],
      "cwd": "/home/rasel/Projects_hub/mcp-server",
      "env": {
        "ANTHROPIC_MODEL": "claude-3-5-sonnet-20241022",
        "CHROMA_PERSIST_DIRECTORY": "/home/rasel/Projects_hub/mcp-server/chroma_db",
        "LOG_LEVEL": "INFO"
      }
    }
  }
}

TIP

After modifyingclaude_desktop_config.json, fully quit and restart Claude Desktop. The hammer icon in the bottom right of the input box will display the registered female-wellness tools.

11. Female Health & Medical Safety Engine

Health guidance requires deterministic safeguards. The SafetyService operates upstream of the RAG pipeline and LLM, ensuring safety rules are executed with zero hallucination risk:

User Question
      │
      ▼
┌──────────────────────────────────────────────┐
│  Safety Gate (safety_service.py)             │
│  • Preeclampsia screening (headache + aura)  │
│  • Hemorrhage / Ectopic screening            │
│  • DVT screening (unilateral calf swelling)  │
│  • Acute Cardiac screening (chest pain)      │
│  • Postpartum Sepsis (fever + foul lochia)   │
│  • Prescription dosage adjustment blocker    │
│  • Self-diagnosis request blocker            │
└──────┬────────────────────────────────┬──────┘
       │                                │
  [Red Flag Detected]              [Safe Query]
       │                                │
       ▼                                ▼
Immediate Clinical Alert         Proceed to RAG & LLM
(Halts general chit-chat)

Safety Rules Enforced:

Educational Scope: Guidance strictly supports lifestyle, nutrition, and wellness habits.
Never Diagnose: Inquiries asking "Do I have endometriosis/PCOS?" are redirected to licensed healthcare providers for clinical imaging and biomarker tests.
Never Prescribe or Modify Dosages: Questions requesting medication dosages (e.g. Metformin, Levothyroxine) are blocked.
Physiological Individuality: Never assumes all women have regular 28-day cycles, nor that all users are fertile, sexually active, or pregnant.
Privacy by Default: The system never infers pregnancy or menopausal status automatically. Only user-provided profile context is incorporated.

12. Testing

The project maintains a 100% passing test suite across all 10 phases. External LLM calls are mocked, allowing tests to run completely offline without an API key.

# Run all 74 unit and integration tests
pytest tests/ -v

# Run with coverage report
pytest --cov=src/female_wellness tests/

# Run linter checks
ruff check src tests scripts

Test Suite Coverage

Test Module	Tests	Focus Area
`test_config.py`	9	Pydantic settings, overlap validation, secret masking, stderr logging
`test_document_loader.py`	6	PDF, DOCX, TXT, MD loading, life-stage inference, error handling
`test_chunking.py`	4	Sentence boundary retention, overlap seeding, Chroma serialization
`test_embeddings.py`	6	384-dimension vector output, unit normalization, similarity ranking
`test_vector_store.py`	8	ChromaDB CRUD, persistence, cosine distance metric, metadata filtering
`test_retriever.py`	5	Semantic retrieval, life stage isolation, similarity cutoffs, RAG context
`test_life_stages.py`	8	All 6 journeys, normalization, feature specs, privacy non-inference
`test_anthropic_service.py`	7	Grounded answer generation, refusal on empty evidence, rate limit & timeout handling
`test_mcp_tools.py`	10	Live execution of all 7 tools, 3 resources, and 3 prompt templates
`test_safety.py`	11	Preeclampsia, DVT, hemorrhage, cardiac alerts, dosage blocking, disclaimers
Total	74	100% Passing

13. Troubleshooting & Common Issues

1. Claude Desktop shows `Server disconnected` or JSON-RPC errors

Cause: Standard output (stdout) was polluted by third-party print statements or library logs.
Solution: Ensure all logging uses female_wellness.core.logging_config.get_logger(). Logs must write strictly to sys.stderr.

2. `ModuleNotFoundError: No module named 'female_wellness'`

Cause: The package was not installed into the active virtual environment.
Solution: Activate your .venv and run pip install -e . from the repository root.

3. `AnthropicAPIKeyMissingError` when invoking `ask_wellness_question`

Cause: No Anthropic API key was configured in .env.
Solution: Provide an ANTHROPIC_API_KEY=sk-ant-... in .env for server-side generation. If running in Claude Desktop, Claude Desktop can reason over search_wellness_knowledge directly without needing a server-side key.

4. `CHUNK_OVERLAP must be strictly less than CHUNK_SIZE`

Cause: Misconfigured chunk settings in .env.
Solution: Ensure CHUNK_OVERLAP < CHUNK_SIZE (default is 800 size, 150 overlap).

14. Production Roadmap & Future Improvements

PostgreSQL pgvector Migration: Swap ChromaVectorStore with a PostgreSQL pgvector implementation using the modular BaseVectorStore interface for distributed multi-tenant workloads.
Hybrid Search (BM25 + Dense Vectors): Integrate sparse lexical search (BM25 or Reciprocal Rank Fusion) alongside dense BGE embeddings to enhance keyword retrieval for rare medical terminology.
Async Batch Ingestion: Leverage asyncio worker pools for parallelizing multi-thousand document PDF parsing during enterprise document ingestion.
Biometric Device Ingestion: Add wearable data loaders (Apple HealthKit, Oura, Whoop) to stream real-time HRV and resting heart rate data into the Athlete Performance journey.

15. License

This project is licensed under the MIT License — see the LICENSE file for details.

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Female Wellness & Lifestyle MCP Server

1. Project Overview

2. System Architecture

Architectural Diagram

Critical Protocol Rule: STDIO Purity

3. Technology Stack

4. Directory Structure

5. Installation & Setup

Prerequisites

Step-by-Step Installation

6. Environment Configuration

Configurable Variables

7. Knowledge Ingestion CLI

Supported Document Formats

Running Ingestion

8. MCP Tools, Resources, and Prompts Catalog

A. MCP Tools

B. MCP Resources

C. MCP Prompts

9. Running the MCP Server

Standalone CLI Execution

10. Claude Desktop Integration

Locating Your Claude Desktop Configuration

Configuration Snippet

11. Female Health & Medical Safety Engine

Safety Rules Enforced:

12. Testing

Test Suite Coverage

13. Troubleshooting & Common Issues

1. Claude Desktop shows Server disconnected or JSON-RPC errors

2. ModuleNotFoundError: No module named 'female_wellness'

3. AnthropicAPIKeyMissingError when invoking ask_wellness_question

4. CHUNK_OVERLAP must be strictly less than CHUNK_SIZE

14. Production Roadmap & Future Improvements

15. License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

healthcare-ai

Clarity MCP

Works With Agents MCP Server

MCP Health Coach

1. Claude Desktop shows `Server disconnected` or JSON-RPC errors

2. `ModuleNotFoundError: No module named 'female_wellness'`

3. `AnthropicAPIKeyMissingError` when invoking `ask_wellness_question`

4. `CHUNK_OVERLAP must be strictly less than CHUNK_SIZE`