How to Set Up a Python MCP Server for In-House Legacy Systems with FastMCP in 10 Minutes

Subtitle: LLM Internal System Integration | Practical Guide to Model Context Protocol Implementation

You do not need to replace decades-old in-house systems. With FastMCP, you can create an environment in just 10 minutes where LLMs like Claude or GPT-4 can converse directly with in-house systems using natural language by overlaying AI interfaces onto existing legacy code. You can implement scenarios much faster than expected, such as an AI automatically querying the HR system to answer the question, "Tell me Team Leader Kim Cheol-su how many days of annual leave he has left this year."

By following this guide, you can attach the @mcp.tool decorator to your existing in-house system to build an MCP server and complete a demo querying a legacy database in natural language from Claude Desktop within 10 minutes. The explanation covers everything from basic tool declarations to automatic OpenAPI conversion, multi-server integration, and security considerations, all at a level ready for immediate practical application.

If you already have REST API or database connection code, you can reuse it as is. The only new thing to create is a few lines of decorators.

Target Audience for this Article: Python backend developers. Uses Python concepts such as async/await, decorators, and session pools. If you are new to Python, it is recommended that you check the "What is a Python Decorator?" callout below first.

Key Concepts

Problems Solved by MCP: N×M Integration Hell

To connect AI models with internal systems, it was previously necessary to create a custom plugin for each system. If there are three AI models and five systems to connect, up to 15 connectors are required. MCP solves this "N×M integration problem."

MCP (Model Context Protocol) — An open standard released by Anthropic in November 2024. It is a protocol that enables LLMs to communicate with external tools, data sources, and systems in a standardized manner. Once you create an MCP server, it can be used by all AI clients that support MCP, such as Claude, GPT-4, and Azure AI Agent.

As of 2025, both OpenAI and Microsoft Azure have officially adopted MCP, and the number of publicly available MCP servers has exceeded 5,800. Monthly SDK downloads have surpassed 97 million, establishing it as the de facto industry standard for AI-system integration.

Three Components of FastMCP

FastMCP is a high-level framework that enables you to build MCP servers in a Pythonic way. FastMCP 1.0 was integrated into the official MCP Python SDK, and the currently independently managed 2.x version is downloaded more than 1 million times a day.

The MCP server consists of three core components.

Component	Decorator	Role
Tools	`@mcp.tool`	Functions called by the LLM — DB queries, API calls, calculations, etc.
Resources	`@mcp.resource`	Read-only data exposure — files, settings, schemas, etc.
Prompts	`@mcp.prompt`	Reusable prompt templates for complex tasks

This article focuses on @mcp.tool, which is the most widely used in practice. @mcp.resource is introduced in Example 2 for schema exposure purposes, and @mcp.prompt is used for automating repetitive workflows but is excluded from the scope of this article.

Why 10 Minutes Is Possible

There are two key points.

Decorator-based Tool Registration — Simply appending @mcp.tool to an existing function makes it a tool recognized by LLM. No separate schema definition or JSON serialization code is required.
Automatic OpenAPI Spec Conversion — Use FastMCP.from_openapi() to convert legacy REST APIs with Swagger/OpenAPI specifications into an MCP server in a single line.

python

# pip install fastmcp  또는  uv add fastmcp
 
from fastmcp import FastMCP
# from legacy_hr_module import legacy_hr_db  ← 기존 DB 연결 모듈 그대로 가져오기
 
mcp = FastMCP("사내 시스템 MCP 서버")
 
@mcp.tool
def query_employee(employee_id: str) -> dict:
    """레거시 HR 시스템에서 직원 정보를 조회합니다."""
    return legacy_hr_db.get_employee(employee_id)  # 기존 코드 재사용
 
if __name__ == "__main__":
    mcp.run()  # 로컬은 stdio, 원격 배포는 transport="http"

What is a Python Decorator? — An expression starting with @ above a function, like @mcp.tool, is a decorator. It is Python syntax that wraps a function to grant additional behavior; in this case, it means "register this function as an MCP Tool." It is conceptually equivalent to Java's @RestController and Spring's @GetMapping.

stdio vs HTTP Transport — stdio connects with local clients like Claude Desktop via inter-process communication. transport="http" deploys to a remote server, allowing multiple clients to access it over the network. We recommend stdio for prototypes and HTTP for production.

Practical Application

Example 1: Immediately converting a legacy REST API to an OpenAPI

If you have Swagger documentation in your internal system, you can automatically generate an MCP server with a single line of code. All API endpoints are automatically mapped to the Tool without the need to define the Tool manually.

python

import asyncio
import httpx
from fastmcp import FastMCP
 
async def main():
    # AsyncClient를 async with로 관리해 이벤트 루프 종료 시 안전하게 정리
    async with httpx.AsyncClient(base_url="http://legacy-hr-system") as client:
        # OpenAPI 스펙을 가져와 MCP 서버 자동 생성
        response = await client.get("/openapi.json")
        openapi_spec = response.json()
 
        mcp = FastMCP.from_openapi(
            openapi_spec=openapi_spec,
            client=client,
            name="HR-MCP"
        )
        # run_async()로 동일한 이벤트 루프 안에서 서버 실행
        await mcp.run_async(transport="http")
 
if __name__ == "__main__":
    asyncio.run(main())

Code Element	Role
`async with httpx.AsyncClient`	Safely clean up clients when the event loop closes
`FastMCP.from_openapi()`	Automatically convert OpenAPI specifications to a tool
`mcp.run_async()`	Run server within the same event loop (prevent loop crashes)

Now, if you enter "Tell me Team Leader Kim Cheol-su's remaining vacation days for this year" in Claude, the MCP server automatically calls the legacy HR API and returns the result.

If the REST API conversion has been completed, how should we handle cases where we need to connect directly to the DB without an API?

Example 2: Legacy Oracle DB Direct Query Tool

The most common form encountered in legacy systems is a direct connection to an Oracle DB. In cloud-native environments, the same pattern can be applied to PostgreSQL or MySQL; Oracle was chosen as an example because it is the most prevalent in legacy environments within the finance and manufacturing sectors.

You can declare the function with the @mcp.tool decorator, and reuse existing connection code such as cx_Oracle.

python

import os
import cx_Oracle
from fastmcp import FastMCP
 
mcp = FastMCP("Oracle DB MCP 서버")
 
# DB 연결 풀 초기화 (기존 코드)
# 자격증명은 환경 변수로 관리하고 코드에 직접 쓰지 않습니다
pool = cx_Oracle.SessionPool(
    user="readonly_user",
    password=os.getenv("DB_PASSWORD"),  # 환경 변수에서 읽기
    dsn="legacy-oracle:1521/PROD"
)
 
@mcp.tool
def get_inventory(product_code: str, warehouse_id: int) -> dict:
    """
    창고별 재고 현황을 조회합니다.
 
    Args:
        product_code: 제품 코드 (예: 'PROD-001')
        warehouse_id: 창고 ID
    """
    with pool.acquire() as conn:
        cursor = conn.cursor()
        cursor.execute(
            "SELECT stock_qty, last_updated FROM inventory "
            "WHERE product_code = :1 AND warehouse_id = :2",
            [product_code, warehouse_id]
        )
        row = cursor.fetchone()
        if row:
            return {"stock_qty": row[0], "last_updated": str(row[1])}
        return {"error": "재고 정보를 찾을 수 없습니다."}
 
@mcp.resource("inventory://schema")
def get_schema() -> str:
    """재고 테이블 스키마 정보를 반환합니다."""
    return "inventory(product_code VARCHAR, warehouse_id INT, stock_qty INT, last_updated DATE)"
 
if __name__ == "__main__":
    mcp.run()

Dogstrings Are LLM Guides — @mcp.tool A function's docstring is a description that the LLM refers to when selecting a tool. The more specifically you write about when to use the tool and what the argument types are, the higher the accuracy of the LLM's tool selection.

SQL Injection Prevention — Using bind variables such as :1 and :2 prevents user input from being directly inserted into SQL queries. It is recommended to always process external input received from tools in this way.

If you have established a direct DB connection, situations frequently arise where you need to link multiple systems, such as Jira, Slack, and ERP, into a single endpoint.

Example 3: Multi-Server Integration Gateway

When an MCP server is configured for each system, it can be integrated into a single gateway using mcp.mount(). The AI agent accesses all on-premises systems by knowing only one endpoint.

python

from fastmcp import FastMCP
 
# 각 시스템별 서버 (별도 파일 또는 모듈로 관리)
from servers.jira_server import jira_mcp
from servers.slack_server import slack_mcp
from servers.erp_server import erp_mcp
 
# 통합 게이트웨이 서버
gateway = FastMCP("사내 통합 게이트웨이")
 
# prefix를 지정하면 도구 이름이 충돌하지 않음
# FastMCP 2.x 버전에 따라 mount() 인자 이름이 다를 수 있으므로
# 공식 문서(gofastmcp.com)에서 현재 버전의 API를 확인하는 것을 권장합니다
gateway.mount("/jira", jira_mcp)
gateway.mount("/slack", slack_mcp)
gateway.mount("/erp", erp_mcp)
 
if __name__ == "__main__":
    # 하나의 엔드포인트로 전체 사내 시스템 접근 가능
    gateway.run(transport="http", port=8000)

Components	Description
Jira tools are registered under the `gateway.mount("/jira", jira_mcp)`	`/jira` prefix
Single Endpoint	`http://gateway:8000` Access All Systems as One
Prefix Separation	Used to prevent tool name conflicts and as a unit for permission management

Pros and Cons Analysis

Advantages

Item	Content
Rapid Development	Reduce development time by up to 5x with decorator-based registration. Build a server within minutes with minimal code.
Reuse Existing Code	Abstraction of complex MCP specifications. Register existing Python functions as tools as is
Automatic OpenAPI Conversion	Instantly migrate legacy REST APIs with Swagger specifications to an MCP server without code modification
Multi-Server Combination	Integrate multiple MCP servers into a single gateway app with `mount()`
Transport Flexibility	Supports stdio (local), HTTP (remote), and SSE (legacy compatible)
Built-in Debugging	Real-time testing and logging via MCP Inspector (React UI) integration
Built-in Production Features	OAuth 2.1 Authentication, Session Management Built-in

Disadvantages and Precautions

The risks to watch out for are highlighted in bold.

Item	Content	Response Plan
⚠️ Basic Security Vulnerability	Basic HTTP deployments lack authentication and encryption. Even on internal networks, it is vulnerable.	Add an authentication layer using OAuth 2.1 or API Gateway (Kong, Nginx). Detailed configuration will be covered in the next post.
⚠️ RCE Vulnerability Risk	Serious security issues if file access and shell commands are exposed via tools without input validation	Internal and external inputs within the tool must be used only after whitelist validation
Overexposure to Tools	The more tools available, the higher the probability that the LLM will select the wrong tool	Manually remove unnecessary endpoints after automatic OpenAPI conversion
Limitations of Automated Conversion Quality	OpenAPI automated generation servers may experience LLM performance degradation compared to manual design servers	Automated conversion for prototypes, manual optimization of core tools for production
Performance Bottleneck	Response delays may occur in high-traffic environments	Add result caching, establish horizontal scaling (container replication) strategies
Learning Curve for Advanced Features	Lack of Documentation for Advanced Features such as Proxy, Filtering, and Middleware	Actively Utilize Official GitHub Issues and the Discord Community

RCE (Remote Code Execution) — Remote code execution vulnerability. A serious security issue that allows an attacker to execute arbitrary commands on a server. This vulnerability occurs if the tool passes user input directly to subprocess or eval.

The Most Common Mistakes in Practice

To avoid duplication with the items in the disadvantages table, we will focus here on "why this mistake is repeated."

Deploying in HTTP Mode Without Authentication — Since mcp.run(transport="http") produces results quickly, it is often deployed to the internal network in its current state. The mindset that "it is just the internal network anyway" leads to complacency. Since anyone within the company can access it, it is a good habit to configure API Key middleware, even in a development environment.
Omitting or Writing Poorly Written Docstrings — When focusing on writing code, it feels like you will add docstrings later. However, the LLM determines which tool to use and when based on the docstring. If the docstring is inadequate, the LLM may call the wrong tool or not use it at all.
Deploying OpenAPI Automatic Conversion Results to Production Without Review — Because from_openapi() operates quickly, you may be tempted to skip the verification step. Automatically generated servers include unnecessary endpoints, which lowers the accuracy of LLM tool selection. We recommend reviewing the conversion results first with MCP Inspector and going through the process of keeping only the essential tools.

In Conclusion

FastMCP is the fastest way to overlay AI-understood interfaces without replacing legacy systems. By combining the @mcp.tool decorator with OpenAPI auto-conversion, you can see the first interaction between your legacy system and Claude Desktop converse in natural language within 10 minutes.

3 Steps to Start Right Now:

You can install FastMCP and create your first tool. After installing it with pip install fastmcp or uv add fastmcp, you can append @mcp.tool to an internal DB query function and run it with mcp.run(). If you open the MCP Inspector with npx @modelcontextprotocol/inspector, you can check in real-time whether the tool is being displayed correctly.
If you have Swagger documentation in your internal system, you can try FastMCP.from_openapi(). All endpoints are automatically converted into the tool with just a single OpenAPI JSON URL. It is recommended that you also review the conversion results in the MCP Inspector and clean up unnecessary endpoints.
You can connect with Claude Desktop to operate your internal systems using natural language. If you register the MCP server path in claude_desktop_config.json in Claude Desktop, you can experience conversing with legacy systems directly in the chat window with questions such as "Show me last month's inventory status."

Next Post: We will cover how to enhance FastMCP servers to a production level by applying OAuth 2.1 authentication and OpenTelemetry observability.

Reference Materials

If you are just starting out, check these three places first:

For Advanced Study:

How to Set Up a Python MCP Server for In-House Legacy Systems with FastMCP in 10 Minutes | DEV BAK - 기술블로그

How to Set Up a Python MCP Server for In-House Legacy Systems with FastMCP in 10 Minutes

Subtitle: LLM Internal System Integration | Practical Guide to Model Context Protocol Implementation

If you already have REST API or database connection code, you can reuse it as is. The only new thing to create is a few lines of decorators.

Key Concepts

Problems Solved by MCP: N×M Integration Hell

Three Components of FastMCP

The MCP server consists of three core components.

Component	Decorator	Role
Tools	`@mcp.tool`	Functions called by the LLM — DB queries, API calls, calculations, etc.
Resources	`@mcp.resource`	Read-only data exposure — files, settings, schemas, etc.
Prompts	`@mcp.prompt`	Reusable prompt templates for complex tasks

Why 10 Minutes Is Possible

There are two key points.

Decorator-based Tool Registration — Simply appending @mcp.tool to an existing function makes it a tool recognized by LLM. No separate schema definition or JSON serialization code is required.
Automatic OpenAPI Spec Conversion — Use FastMCP.from_openapi() to convert legacy REST APIs with Swagger/OpenAPI specifications into an MCP server in a single line.

python

# pip install fastmcp  또는  uv add fastmcp
 
from fastmcp import FastMCP
# from legacy_hr_module import legacy_hr_db  ← 기존 DB 연결 모듈 그대로 가져오기
 
mcp = FastMCP("사내 시스템 MCP 서버")
 
@mcp.tool
def query_employee(employee_id: str) -> dict:
    """레거시 HR 시스템에서 직원 정보를 조회합니다."""
    return legacy_hr_db.get_employee(employee_id)  # 기존 코드 재사용
 
if __name__ == "__main__":
    mcp.run()  # 로컬은 stdio, 원격 배포는 transport="http"

Practical Application

Example 1: Immediately converting a legacy REST API to an OpenAPI

python

import asyncio
import httpx
from fastmcp import FastMCP
 
async def main():
    # AsyncClient를 async with로 관리해 이벤트 루프 종료 시 안전하게 정리
    async with httpx.AsyncClient(base_url="http://legacy-hr-system") as client:
        # OpenAPI 스펙을 가져와 MCP 서버 자동 생성
        response = await client.get("/openapi.json")
        openapi_spec = response.json()
 
        mcp = FastMCP.from_openapi(
            openapi_spec=openapi_spec,
            client=client,
            name="HR-MCP"
        )
        # run_async()로 동일한 이벤트 루프 안에서 서버 실행
        await mcp.run_async(transport="http")
 
if __name__ == "__main__":
    asyncio.run(main())

Code Element	Role
`async with httpx.AsyncClient`	Safely clean up clients when the event loop closes
`FastMCP.from_openapi()`	Automatically convert OpenAPI specifications to a tool
`mcp.run_async()`	Run server within the same event loop (prevent loop crashes)

Now, if you enter "Tell me Team Leader Kim Cheol-su's remaining vacation days for this year" in Claude, the MCP server automatically calls the legacy HR API and returns the result.

If the REST API conversion has been completed, how should we handle cases where we need to connect directly to the DB without an API?

Example 2: Legacy Oracle DB Direct Query Tool

You can declare the function with the @mcp.tool decorator, and reuse existing connection code such as cx_Oracle.

python

import os
import cx_Oracle
from fastmcp import FastMCP
 
mcp = FastMCP("Oracle DB MCP 서버")
 
# DB 연결 풀 초기화 (기존 코드)
# 자격증명은 환경 변수로 관리하고 코드에 직접 쓰지 않습니다
pool = cx_Oracle.SessionPool(
    user="readonly_user",
    password=os.getenv("DB_PASSWORD"),  # 환경 변수에서 읽기
    dsn="legacy-oracle:1521/PROD"
)
 
@mcp.tool
def get_inventory(product_code: str, warehouse_id: int) -> dict:
    """
    창고별 재고 현황을 조회합니다.
 
    Args:
        product_code: 제품 코드 (예: 'PROD-001')
        warehouse_id: 창고 ID
    """
    with pool.acquire() as conn:
        cursor = conn.cursor()
        cursor.execute(
            "SELECT stock_qty, last_updated FROM inventory "
            "WHERE product_code = :1 AND warehouse_id = :2",
            [product_code, warehouse_id]
        )
        row = cursor.fetchone()
        if row:
            return {"stock_qty": row[0], "last_updated": str(row[1])}
        return {"error": "재고 정보를 찾을 수 없습니다."}
 
@mcp.resource("inventory://schema")
def get_schema() -> str:
    """재고 테이블 스키마 정보를 반환합니다."""
    return "inventory(product_code VARCHAR, warehouse_id INT, stock_qty INT, last_updated DATE)"
 
if __name__ == "__main__":
    mcp.run()

If you have established a direct DB connection, situations frequently arise where you need to link multiple systems, such as Jira, Slack, and ERP, into a single endpoint.

Example 3: Multi-Server Integration Gateway

When an MCP server is configured for each system, it can be integrated into a single gateway using mcp.mount(). The AI agent accesses all on-premises systems by knowing only one endpoint.

python

from fastmcp import FastMCP
 
# 각 시스템별 서버 (별도 파일 또는 모듈로 관리)
from servers.jira_server import jira_mcp
from servers.slack_server import slack_mcp
from servers.erp_server import erp_mcp
 
# 통합 게이트웨이 서버
gateway = FastMCP("사내 통합 게이트웨이")
 
# prefix를 지정하면 도구 이름이 충돌하지 않음
# FastMCP 2.x 버전에 따라 mount() 인자 이름이 다를 수 있으므로
# 공식 문서(gofastmcp.com)에서 현재 버전의 API를 확인하는 것을 권장합니다
gateway.mount("/jira", jira_mcp)
gateway.mount("/slack", slack_mcp)
gateway.mount("/erp", erp_mcp)
 
if __name__ == "__main__":
    # 하나의 엔드포인트로 전체 사내 시스템 접근 가능
    gateway.run(transport="http", port=8000)

Components	Description
Jira tools are registered under the `gateway.mount("/jira", jira_mcp)`	`/jira` prefix
Single Endpoint	`http://gateway:8000` Access All Systems as One
Prefix Separation	Used to prevent tool name conflicts and as a unit for permission management

Pros and Cons Analysis

Advantages

Item	Content
Rapid Development	Reduce development time by up to 5x with decorator-based registration. Build a server within minutes with minimal code.
Reuse Existing Code	Abstraction of complex MCP specifications. Register existing Python functions as tools as is
Automatic OpenAPI Conversion	Instantly migrate legacy REST APIs with Swagger specifications to an MCP server without code modification
Multi-Server Combination	Integrate multiple MCP servers into a single gateway app with `mount()`
Transport Flexibility	Supports stdio (local), HTTP (remote), and SSE (legacy compatible)
Built-in Debugging	Real-time testing and logging via MCP Inspector (React UI) integration
Built-in Production Features	OAuth 2.1 Authentication, Session Management Built-in

Disadvantages and Precautions

The risks to watch out for are highlighted in bold.

Item	Content	Response Plan
⚠️ Basic Security Vulnerability	Basic HTTP deployments lack authentication and encryption. Even on internal networks, it is vulnerable.	Add an authentication layer using OAuth 2.1 or API Gateway (Kong, Nginx). Detailed configuration will be covered in the next post.
⚠️ RCE Vulnerability Risk	Serious security issues if file access and shell commands are exposed via tools without input validation	Internal and external inputs within the tool must be used only after whitelist validation
Overexposure to Tools	The more tools available, the higher the probability that the LLM will select the wrong tool	Manually remove unnecessary endpoints after automatic OpenAPI conversion
Limitations of Automated Conversion Quality	OpenAPI automated generation servers may experience LLM performance degradation compared to manual design servers	Automated conversion for prototypes, manual optimization of core tools for production
Performance Bottleneck	Response delays may occur in high-traffic environments	Add result caching, establish horizontal scaling (container replication) strategies
Learning Curve for Advanced Features	Lack of Documentation for Advanced Features such as Proxy, Filtering, and Middleware	Actively Utilize Official GitHub Issues and the Discord Community

The Most Common Mistakes in Practice

To avoid duplication with the items in the disadvantages table, we will focus here on "why this mistake is repeated."

Deploying in HTTP Mode Without Authentication — Since mcp.run(transport="http") produces results quickly, it is often deployed to the internal network in its current state. The mindset that "it is just the internal network anyway" leads to complacency. Since anyone within the company can access it, it is a good habit to configure API Key middleware, even in a development environment.
Omitting or Writing Poorly Written Docstrings — When focusing on writing code, it feels like you will add docstrings later. However, the LLM determines which tool to use and when based on the docstring. If the docstring is inadequate, the LLM may call the wrong tool or not use it at all.
Deploying OpenAPI Automatic Conversion Results to Production Without Review — Because from_openapi() operates quickly, you may be tempted to skip the verification step. Automatically generated servers include unnecessary endpoints, which lowers the accuracy of LLM tool selection. We recommend reviewing the conversion results first with MCP Inspector and going through the process of keeping only the essential tools.

In Conclusion

3 Steps to Start Right Now:

You can install FastMCP and create your first tool. After installing it with pip install fastmcp or uv add fastmcp, you can append @mcp.tool to an internal DB query function and run it with mcp.run(). If you open the MCP Inspector with npx @modelcontextprotocol/inspector, you can check in real-time whether the tool is being displayed correctly.
If you have Swagger documentation in your internal system, you can try FastMCP.from_openapi(). All endpoints are automatically converted into the tool with just a single OpenAPI JSON URL. It is recommended that you also review the conversion results in the MCP Inspector and clean up unnecessary endpoints.
You can connect with Claude Desktop to operate your internal systems using natural language. If you register the MCP server path in claude_desktop_config.json in Claude Desktop, you can experience conversing with legacy systems directly in the chat window with questions such as "Show me last month's inventory status."

Next Post: We will cover how to enhance FastMCP servers to a production level by applying OAuth 2.1 authentication and OpenTelemetry observability.

Reference Materials

If you are just starting out, check these three places first:

For Advanced Study:

Key Concepts

Problems Solved by MCP: N×M Integration Hell

Three Components of FastMCP

Why 10 Minutes Is Possible

Practical Application

Example 1: Immediately converting a legacy REST API to an OpenAPI

Example 2: Legacy Oracle DB Direct Query Tool

Example 3: Multi-Server Integration Gateway

Pros and Cons Analysis

Advantages

Disadvantages and Precautions

The Most Common Mistakes in Practice

In Conclusion

Reference Materials

Key Concepts

Problems Solved by MCP: N×M Integration Hell

Three Components of FastMCP

Why 10 Minutes Is Possible

Practical Application

Example 1: Immediately converting a legacy REST API to an OpenAPI

Example 2: Legacy Oracle DB Direct Query Tool

Example 3: Multi-Server Integration Gateway

Pros and Cons Analysis

Advantages

Disadvantages and Precautions

The Most Common Mistakes in Practice

In Conclusion

Reference Materials

Recommended Posts

Production MCP Server Implementation Patterns: Mastering OAuth 2.1 Authentication and OpenTelemetry Tracing Based on FastMCP 3.0

Building AI Agent Pipelines with n8n: From Automatic Email Classification to RAG Pipelines

Why the AI Agent Deleted the Production DB — How to Enhance VibeCoding Security with an ABAC Hybrid Model

RBAC in the Era of VibeCoding: 3 Layers for Designing AI Agent Authority

Connecting Slack, Notion, and Vector DB to Claude Agents with MCP — A Practical Guide to Building Incident Automation and RAG Pipelines

Controlling AI Agent Authority with OPA and Cedar: A Practical Guide to Policy-as-Code