我构建了 OmniAgent：MCP 与自定义业务逻辑之间缺失的桥梁
模型上下文协议（MCP）生态系统正在迅速发展——包括Git服务器、数据库、API和文件系统。但存在一个空白：如何将MCP工具与您现有的业务逻辑结合起来？
我有MCPOmni Connect——一个强大的MCP客户端，具备隐藏的代理功能（ReAct和Orchestrator模式）。但开发者们总是面临同样的问题：
- “我可以连接到MCP服务器，但如何添加我的Python函数？”
- “代理可以读取文件和查询数据库，但它不知道我的业务规则。”
- “我需要能够理解MCP工具和我的自定义逻辑的代理。”
架构差距
MCPOmni Connect拥有强大的代理功能，但在CLI命令中隐藏，缺少了连接的桥梁：
MCP生态系统 ← 缺失的桥梁 → 您的业务逻辑
其他平台要么：
- 提供自定义工具但没有MCP集成
- 提供MCP集成但没有自定义工具系统
- 同时提供两者但没有智能编排
OmniAgent解决方案：本地工具 + MCP + 智能记忆
我没有再构建一个新的AI平台，而是通过添加缺失的桥梁来完善已有的强大功能：
本地工具系统：将Python函数注册为AI工具
```python
@tool_registry.register_tool("calculate_shipping_cost")
def calculate_shipping_cost(weight: float, zone: str) -> str:
# 您的业务逻辑
return f"运费: ${cost}"
```
现在，AI可以将您的业务逻辑与MCP工具结合使用。
智能编排：代理理解何时使用：
- MCP工具（文件系统、数据库、API）
- 您的自定义业务逻辑
- 两者的组合
多层记忆：代理记住：
- 哪些工具组合适用于您的特定用例
- 您的业务上下文和偏好
- 之前对类似问题的解决方案
与众不同之处
1. MCP + 自定义工具集成
其他平台让您做出选择，而OmniAgent则将两者结合：
- 完整的MCP生态系统访问（100多种服务器类型）
- 注册您现有的Python函数
- 两者之间的智能编排
2. 业务逻辑理解
通用AI代理无法理解您的领域。通过本地工具：
- 代理学习您的业务规则
- 记住您的特定工作流程
- 将外部数据（MCP）与内部逻辑（您的工具）结合
3. 为两者构建的生产基础设施
- 事件流监控MCP和自定义工具的使用
- 向量记忆，记住成功的MCP + 自定义工具组合
- 可以自主使用您业务逻辑的后台代理
演变路径
MCPOmni Connect（之前）：
- 强大的MCP客户端，具备隐藏的代理功能
- CLI中的ReAct和Orchestrator模式
- 无法添加自定义业务逻辑
OmniAgent（之后）：
- 相同的MCP基础 + 本地工具桥梁
- 为两种工具类型提供智能记忆
- 完整的可编程平台
真实用例：电子商务代理
```python
# 您的业务逻辑
@tool_registry.register_tool("check_inventory")
def check_inventory(product_id: str) -> str:
return inventory_status
@tool_registry.register_tool("calculate_discount")
def calculate_discount(customer_tier: str, amount: float) -> str:
return discount_amount
# 代理结合：
# - MCP文件系统工具（读取订单文件）
# - MCP数据库工具（客户数据）
# - 您的业务逻辑（库存、定价规则）
```
技术基础
- 工具集成：MCP生态系统与本地Python函数之间的无缝桥梁
- 记忆系统：向量数据库记住成功的工具组合
- 事件流：监控MCP和自定义工具的使用
- 执行引擎：基于XML的编排用于复杂工具链
真正的创新
不是另一个AI代理平台，而是一个桥梁，让您结合：
- 不断增长的MCP生态系统
- 您现有的业务逻辑
- 学习您模式的智能记忆
GitHub: https://github.com/Abiorh001/mcp_omni_connect
查看原文
The Model Context Protocol (MCP) ecosystem is exploding - servers for Git, databases, APIs, filesystems. But there was a gap: How do you combine MCP tools with your existing business logic?<p>I had MCPOmni Connect - a powerful MCP client with hidden agent capabilities (ReAct & Orchestrator modes). But developers kept hitting the same wall:<p>- "I can connect to MCP servers, but how do I add MY Python functions?"
- "The agent can read files and query databases, but it doesn't know my business rules"
- "I need agents that understand both MCP tools AND my custom logic"<p>The Architecture Gap<p>MCPOmni Connect had powerful agent capabilities buried in CLI commands, but was missing the bridge between:<p>MCP Ecosystem ←Missing Bridge → Your Business Logic<p>Other platforms either:
- Give you custom tools but no MCP integration
- Give you MCP integration but no custom tool system
- Give you both but no intelligent orchestration between them<p>The OmniAgent Solution: Local Tools + MCP + Intelligent Memory<p>Instead of building another AI platform, I completed what was already powerful by adding the missing bridge:<p>LOCAL TOOLS SYSTEM: Register Python functions as AI tools
```python
@tool_registry.register_tool("calculate_shipping_cost")
def calculate_shipping_cost(weight: float, zone: str) -> str:
# Your business logic here
return f"Shipping cost: ${cost}"<p>Now AI can use YOUR business logic alongside MCP tools
```<p>INTELLIGENT ORCHESTRATION: Agents understand when to use:
- MCP tools (filesystem, databases, APIs)
- Your custom business logic
- Combinations of both<p>MULTI-TIER MEMORY: Agents remember:
- Which tool combinations work for your specific use cases
- Your business context and preferences
- Previous solutions to similar problems<p>What Makes This Different<p>1. MCP + Custom Tools Integration
Other platforms make you choose. OmniAgent bridges both:
- Full MCP ecosystem access (100+ server types)
- Register your existing Python functions
- Intelligent orchestration between both<p>2. Business Logic Understanding
Generic AI agents don't understand your domain. With local tools:
- Agents learn your business rules
- Remember your specific workflows
- Combine external data (MCP) with internal logic (your tools)<p>3. Production Infrastructure Built for Both
- Event streaming for monitoring both MCP and custom tool usage
- Vector memory that remembers successful MCP + custom tool combinations
- Background agents that can autonomously use your business logic<p>The Evolution Path<p>MCPOmni Connect (Before):
- Powerful MCP client with hidden agent capabilities
- ReAct & Orchestrator modes in CLI
- No way to add custom business logic<p>OmniAgent (After):
- Same MCP foundation + local tools bridge
- Intelligent memory for both tool types
- Complete programmable platform<p>Real Use Case: E-commerce Agent<p>```python
# Your business logic
@tool_registry.register_tool("check_inventory")
def check_inventory(product_id: str) -> str:
return inventory_status<p>@tool_registry.register_tool("calculate_discount")
def calculate_discount(customer_tier: str, amount: float) -> str:
return discount_amount<p># Agent combines:
# - MCP filesystem tools (read order files)
# - MCP database tools (customer data)
# - YOUR business logic (inventory, pricing rules)
```<p>Technical Foundation<p>- Tool Integration: Seamless bridge between MCP ecosystem and local Python functions
- Memory System: Vector databases remember successful tool combinations
- Event Streaming: Monitor both MCP and custom tool usage
- Execution Engine: XML-based orchestration for complex tool chains<p>The Real Innovation<p>Not another AI agent platform. A bridge that lets you combine:
- The growing MCP ecosystem
- Your existing business logic
- Intelligent memory that learns your patterns<p>GitHub: https://github.com/Abiorh001/mcp_omni_connect