MCP stands for Model Context Protocol, which is an open standard protocol launched and open-sourced by Anthropic at the end of 2024. It has now become one of the most popular infrastructures in the AI field.
Using a common analogy: MCP is the "USB-C port" of the AI world.
Why is MCP Needed?
In the past, if you wanted an AI assistant (Agent) to connect to external tools (such as GitHub, local file systems, PostgreSQL databases, Slack, etc.), developers had to write proprietary integration code for "every single AI application" and "every single tool". If there were 10 AI applications and 10 tools, writing 100 separate interfaces was necessary (an N × M integration nightmare).
With MCP, tool developers only need to develop an "MCP Server" (equivalent to a USB-C device) according to the MCP standard, while any AI application that supports MCP (such as Cursor, various Agent frameworks) only needs a built-in "MCP Client" (equivalent to a USB-C port) to achieve plug-and-play. This simplifies the complex N × M development work into N + M.
Core Architectural Attributes of MCP
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Unified Standardized Protocol (No More Reinventing the Wheel) MCP provides a common specification (based on JSON-RPC). Whether reading local files, querying databases, or calling third-party SaaS APIs, the AI can comprehend and invoke them through the same protocol logic. This significantly lowers the barrier for tool integration in Agent development, letting developers focus on the core logic of the Agent instead of writing tedious API integration glue code.
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Support for Three Core Capabilities MCP standardized three core primitives that enable the AI to execute actions, read data, and follow structured playbooks:
- Tools: Allows the AI to execute operations. Example: Running a script, creating a task in Jira, writing data to a database.
- Resources: Allows the AI to read external data. Example: Retrieving file lists from a Git repository, querying specific vector database embeddings as context.
- Prompts: Provides pre-defined templates, letting users or the AI trigger complex workflows standardly.
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Client-Server Architecture and Decoupling MCP uses a Client-Server architecture:
- MCP Host: The AI application you use (such as IDEs, Agent platforms).
- MCP Client: The component inside the Host responsible for maintaining a 1:1 connection with the Server.
- MCP Server: A lightweight, independent application dedicated to exposing tools or data capabilities. This decoupling means you can swap out the underlying LLM or add new data sources at any time without rebuilding your entire Agent setup.
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Local-First and Security (Privacy Friendly) MCP supports communication via standard input/output (stdio) or local HTTP. This means your MCP Server can run entirely on your local machine. Sensitive data (such as local code repositories, private database entries, e-commerce backend metrics) does not need to upload to cloud servers. The AI model only fetches the necessary context when reasoning is required, dramatically improving data security.
Loading a Connector
Currently, connectors for QQ Mail, Tencent Docs, Tencent Lexiang, Tencent Meeting, TAPD, etc. are supported.
For example, loading the Tencent Meeting Connector:
Creating a Task
"Help me create a meeting for tomorrow at 3 PM, topic 'Project Discussion', duration 1 hour."
Creation Successful.
Creating a Custom Connector
Click "Custom Connector" in the top right corner of the Connector Management Page, follow the wizard to configure MCP settings (including service URL and authentication methods). The UI will notify you that the access scope of the custom connector is configured by the user.
References
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