MCP (Model Context Protocol)
What is MCP (Model Context Protocol): an open protocol for connecting AI applications to external tools and data sources. Covers how it works, its core value, and connecting to an MCP Server with MCP
What Is MCP?
MCP stands for Model Context Protocol and is an open protocol for connecting AI applications to external tools and data sources. Applications that support MCP can use the same rules to connect to tools provided by MCP servers, regardless of which large language model they use, such as Anthropic Claude, OpenAI GPT, or Google Gemini.
What Can MCP Help With?
With MCP, an LLM can retrieve external data and operate external systems in addition to answering questions:
Connect to External Data Sources in Real Time
Access databases
Read file systems
Integrate cloud services
Actively Invoke Tools
Execute code
Operate applications
How MCP Works
The diagram below shows how MCP works, with the following layers:
User Interface (Frontend)
Users issue instructions through a UI or SDK
AI Model Layer
The LLM determines which tools to call and which parameters to use based on tool definitions; the application's MCP Client converts these into MCP requests and sends them
Protocol Layer (MCP)
Exchanges messages using JSON-RPC 2.0, describes tool parameters with JSON Schema, and defines transports such as stdio and Streamable HTTP
JSON-RPC 2.0 is a communication protocol that defines the structure of "requests and responses" in JSON, allowing LLMs to communicate with different programs using the same language.
MCP Servers
Receive model requests and interact with actual tools or data systems In addition to hosting your own MCP Servers, you can use Remote MCP services to connect LLMs with other services.
External Systems
Real-world resources such as databases, APIs, Git, and file systems
MCP Workflow Diagram
With this flow, an application only needs to implement an MCP Client once to connect to tools provided by various MCP servers.
MCP Use Cases
Use Case 1: Organize Google Drive Files Through MCP—Document Search and Summarization
Describe the task to the LLM in natural language:
Find all documents in Google Drive related to the "2024 Annual Budget" and compile a summary report
The LLM determines which MCP tools to call based on the request, such as search_files. The tool names and results below are illustrative.
MCP Workflow:
Actions the AI Performs:
🔍 Search for documents containing the keyword "2024 Annual Budget"
📄 Read the contents of the 5 documents found
🧠 Analyze the key points of each document
📊 Generate a consolidated summary report
💡 Provide budget analysis recommendations
Results for the User:
Use Case 2: Connect Google Sheet and Slack Through MCP—Schedule Weekly Reports and Send Them to a Group
An LLM can connect tools from multiple external services through MCP, as shown in the diagram below:
Use a scheduling service to trigger the LLM to perform the same query task every week:
The LLM determines which MCP tools to call based on the request, such as search_sheet and query_sheet (illustrative). In MaiAgent, use Agent Scheduling to trigger an Agent to perform these tasks regularly.
Weekly Agent workflow:
🔍 Search for the "Sales Data" spreadsheet
📄 Read the values in the spreadsheet
📊 Generate a consolidated summary report based on the requirements, such as average revenue and growth compared with the same period
💡 Automatically send the consolidated results to a Slack channel
Core Value of MCP
🧠 LLMs Provide Intelligence
Natural language understanding
Reasoning and decision-making
Knowledge integration
Creative generation
Anomaly detection
Predictive analysis
📡 MCP Provides Capabilities
Connect to various tools
Perform actual operations
Integrate across systems
Standardize communication
The LLM understands requirements and decides the next step, while MCP connects tools in a standard way. Together, they enable the LLM to look up data and perform operations during a conversation. The MCP protocol specifies connection and authorization flows; what connected tools can actually do depends on the MCP server implementation and the permissions of the account authorized to use it.
Using MCP in MaiAgent
MaiAgent is an MCP Client: Create an MCP tool in the management console, enter the server URL, and attach it to an Agent so the Agent can call the tools provided by the server. To let AI tools such as Claude and Cursor operate MaiAgent in the other direction, see MaiAgent MCP.
Supported Transports
MaiAgent connects only to remote MCP servers and determines the transport from the URL: it uses SSE when the path ends in /sse, contains /sse/, or the query string includes transport=sse; all other URLs use Streamable HTTP.
Streamable HTTP
Standard transport in the MCP specification: each message is sent to a single endpoint via HTTP POST, and responses can be JSON or a stream
✅
SSE (HTTP with SSE)
Transport from the 2024-11-05 specification that streams server messages using Server-Sent Events
✅
stdio
Standard transport in the MCP specification: the client starts a server process locally (for example, with npx) and communicates through standard input and output
❌
Custom Transports Such as WebSocket
Not among the standard transports in the MCP specification
❌
stdio is unsupported because the platform does not run local commands such as npx or node on its servers. For an MCP server that provides only a stdio version, use the provider's remote version or deploy it yourself as an HTTP service. For transport, URL, and authentication requirements for self-hosted MCP Servers, see Self-Hosted MCP Server Integration Specifications.
Quick Integration Through the Graphical Interface
MaiAgent provides a graphical interface for connecting to remote MCP servers without writing code: when adding an MCP tool in the management console, enter the server URL and authentication method (put a static key in the headers, or select an OAuth connector), then select the tools the Agent may use from the list returned by the server.
Platform Behavior
Tool Scope: Only tools enabled in the MCP tool's "Allowed Tools (JSON Array)" are passed to the model; new tools added by the server are not enabled automatically.
Timeouts and Retries: The MCP session read timeout is 30 seconds. For temporary errors such as disconnected connections, automatic retries apply only to tools that are safe to resend.
Records: Each call is recorded in the tool execution logs, available under AgentOps → Tool Execution Logs.
Connection Validation: MCP tools configured with a URL connect to the server and retrieve its tool list when saved. If the connection fails, "MCP tool validation failed" appears and the incomplete configuration is not saved; tools using OAuth connectors retrieve the list after authorization is complete. For instructions, see Create an MCP Tool in the user manual.
Troubleshooting unhandled errors in a TaskGroup
When the error message in a tool execution log contains unhandled errors in a TaskGroup (1 sub-exception), it is not a separate error type: the MCP client runs its session in an asynchronous task group, and transport-layer errors such as disconnections, read timeouts, and server-initiated connection closures are wrapped in this outer message. The actual cause appears in the sub-exception after the parentheses. Troubleshoot in this order:
Go to AgentOps → Tool Execution Logs, open the record, and check the full
errorMessageandstatus(timeoutmeans no response was received within 30 seconds).The platform automatically retries transport-layer errors. If retries still fail, first confirm that the server URL is reachable from the public internet, the transport (SSE/Streamable HTTP) matches the server, and the headers or OAuth authorization are still valid.
If the sub-exception is an application error returned by the server (for example, a tool reports invalid parameters), the platform does not retry; reproduce the issue by calling your MCP server directly with the same parameters.
If retrieving the tool list again on the tool settings page succeeds, the connection and authentication are working. The issue is usually a timeout or server-side error during execution of that tool.
For considerations when using hosted services such as Composio and Zapier, see Remote MCP Services Overview.
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