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MCP Server — FastAPI

by vandien284
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Python

Model Context Protocol (MCP) Server — FastAPI

A FastAPI-based MCP Server that exposes automation and AI tools via both REST API and MCP protocol. Tools cover performance testing (JMeter), security scanning (OWASP ZAP), browser automation (Playwright, Selenium), code review (GitLab), AI code generation (unit tests, scenario tests, API specs), knowledge retrieval (RAG/Knowledge Base), and design collaboration (Miro).

Table of Contents

  1. Prerequisites

  2. Project Structure

  3. Environment Variables

  4. Start the Server

  5. API & MCP Access

  6. Architecture

  7. Available Tools (MCP)

  8. Development

Related MCP server: Awesome MCP FastAPI

1. Prerequisites

  • Docker

  • Docker Compose (included with Docker Desktop)

2. Project Structure

├── functions/
│   └── main.py                  # FastAPI entry point; registers all routers; configures MCP
├── routers/                     # One file per domain (REST endpoints)
├── services/                    # Core business logic
├── batch_jobs/
│   ├── base/
│   │   └── subprocess_job_runner.py   # Spawns worker processes; tracks state
│   └── jobs/                    # One job file per async tool
├── schemas/                     # Pydantic request/response models
├── models/                      # DynamoDB models, job registry, OWASP ZAP state
├── constants/
│   └── config.py                # All env-var constants (source of truth)
├── jobs/
│   └── scheduler.py             # APScheduler: background sync jobs
├── prompts/                     # Jinja2 prompt templates (organized by feature)
├── skills/                      # Markdown skill definitions for agent workflows
├── subagents/                   # Agent role documentation for multi-step pipelines
├── ui_s3_manager/               # Sub-app mounted at /ui-s3-manager and /ui-s3
├── data/
│   └── DDL/                     # DynamoDB table creation scripts
├── public/                      # Static files (served at /public)
├── docker/                      # Dockerfiles for fastapi (ZAP uses zaproxy/zap-stable in compose)
├── docker-compose.yml
├── requirements.txt
├── .env.example
└── ruff-guide.md

3. Environment Variables

Copy .env.example to .env and fill in the required values:

cp .env.example .env

Variable

Required

Description

AWS_REGION

Yes

AWS region for S3, DynamoDB, Bedrock, etc.

AWS_ACCESS_KEY_ID

Yes

AWS credentials

AWS_SECRET_ACCESS_KEY

Yes

AWS credentials

S3_BUCKET_NAME

Yes

S3 bucket for uploads and report storage

GitLab host allowlist

Code

Edit constants/gitlab_hosts.py (not .env)

GITLAB_TOKEN

Removed

Replaced by per-request gitlab_token field in tool inputs

REDIS_HOST

Yes*

Redis host for Miro OAuth state and per-user token storage

REDIS_PORT

Yes*

Redis port (default 6379 in Docker Compose)

AUTH_JWT_SECRET

Yes

JWT secret for AuthMiddleware

AWS_BEDROCK_LLM_MODEL

Yes

Bedrock model ID (e.g. anthropic.claude-3-haiku-20240307-v1:0)

SLACK_HOOK

No

Slack incoming webhook URL for job notifications

SLACK_CHANNEL

No

Slack channel for notifications

BACKLOG_DOMAIN

No

Backlog domain for backlog sync jobs

BACKLOG_API_KEY

No

Backlog API key

BACKLOG_PROJECT_KEYS

No

Comma-separated Backlog project keys

LLM_LOCAL_BASE_URL

No

Base URL for local LLM (e.g. Ollama: http://localhost:11434)

LLM_LOCAL_MODEL

No

Local LLM model name (e.g. qwen2.5:3b)

BEDROCK_AGENT_ID

No

AWS Bedrock Agent ID

BEDROCK_AGENT_ALIAS_ID

No

AWS Bedrock Agent Alias ID

RAG_INIT_S3_FOLDER

No

S3 folder to seed the RAG knowledge base on startup

Yes* = required only when using that specific tool group.

See .env.example for all RAG, Selenium, and Redis tuning options.

4. Start the Server

Use the OS-specific wrapper script instead of calling docker compose directly. Each script auto-detects the host architecture and sets the correct platform before delegating to Docker.

macOS (Intel or Apple Silicon)

One-time prerequisite for Apple Silicon (M1/M2/M3):

Docker Desktop → Settings → General → enable "Use Rosetta for x86_64/amd64 emulation on Apple Silicon" → Apply & Restart

Step 1 — Build and start:

# All services
./run.sh up --build

# Minimal setup (fastapi + redis only)
./run.sh up --build fastapi redis

# Run in background
./run.sh up -d fastapi redis

On Apple Silicon you will see:

[run.sh] Apple Silicon (arm64) detected — running linux/amd64 containers via Rosetta 2 emulation

Windows

# All services
.\run.ps1 up --build

# Minimal setup (fastapi + redis only)
.\run.ps1 up --build fastapi redis

# Run in background
.\run.ps1 up -d fastapi redis

Common commands (both OSes)

# Stop all containers
./run.sh down          # macOS
.\run.ps1 down         # Windows

# View logs
./run.sh logs -f fastapi

# Open a shell inside the container
docker exec -it fastapi_app bash

Step 2 — (One-time) Create DynamoDB tables:

docker exec -it fastapi_app python ./data/DDL/create_dynamodb_tables.py

Check container logs:

docker logs fastapi_app

5. API & MCP Access

Interface

URL

REST API docs (Swagger)

http://localhost:8000/docs

MCP endpoint

http://localhost:8000/mcp

S3 File Manager UI

http://localhost:8000/ui-s3-manager

Integrate into an LLM client (MCP config):

{
  "mcpServers": {
    "local-mcp": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

6. Architecture

Request Flow

Client → Router → Service → (optional) Batch Job → DynamoDB / Redis
                                                          ↑
Client polls  GET /api/history/{operation_id}  ───────────┘

  1. Router (routers/) — validates the request schema and calls the service.

  2. Service (services/) — core business logic. Short operations (reading rules, file uploads, Miro API calls) return a result immediately. Long-running operations (test execution, security scans, LLM generation) spawn a batch job and return an operation_id.

  3. Batch Job (batch_jobs/jobs/) — runs in a subprocess via SubprocessJobRunner; writes status to DynamoDB/Redis. Only used for async operations.

  4. Client — polls GET /api/history/{operation_id} (exposed as the get_result MCP tool) until the job resolves. Only needed after async operations.

Infrastructure

Service

Description

Exposed Port

fastapi

Main app (fastapi_app)

8000

owaspzap_1–5

5 parallel OWASP ZAP instances

8080 (internal)

redis

Job state / caching

6379

LLM Integration

Service

Description

LLMService (services/llm_service.py)

Direct AWS Bedrock invocations

LLMLocalService (services/llm_local_service.py)

Local LLM via LLM_LOCAL_BASE_URL / LLM_LOCAL_MODEL

Model IDs are configured via AWS_BEDROCK_LLM_MODEL in constants/config.py.

MCP Exposure

Only operations listed in include_operations inside functions/main.py are exposed as MCP tools. Endpoints tagged "allure" are additionally exposed via include_tags.

When adding a new router endpoint intended for MCP, add its operation_id to include_operations in functions/main.py.

Authentication

AuthMiddleware (services/auth_service.py) is applied globally to all routes. JWT secret is configured via AUTH_JWT_SECRET.

7. Available Tools (MCP)

Domain

Operation IDs

JMeter

run_jmeter

OWASP ZAP

scan_owasp_zap, scan_owasp_zap_scenario, scan_owasp_zap_website

Playwright

get_playwright_scenario_rules, run_playwright_e2e, run_playwright_converse

GitLab

gitlab_review_merge_request

Miro

create_miro_board, get_all_boards, get_miro_mermaid_rules, draw_miro_board

Spec Generator

get_template_design, publish_file, publish_file_ui, publish_file_via_s3, extract_archive, ai_generate_spec

Unit Test Generator

ai_generate_unit_test

Scenario Test Generator

generate_scenario_test

Knowledge Base

knowledge_ask

Allure

all endpoints tagged allure

History

get_result, stop_operation

8. Development

All linting and formatting commands run inside the container:

# Open a shell in the container
docker exec -it fastapi_app bash

# Lint
ruff check .
ruff check . --fix            # auto-fix
ruff check path/to/file.py

# Format
ruff format .
ruff format path/to/file.py
ruff format . --check         # dry run (no changes)

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