Guide

Requirements

Package	Required	Purpose
oridecon	Yes	Core framework
oridecon-contracts	Yes	Protocol definitions
oridecon-ai-agents	Optional	Agent system bridging
oridecon-ai-skills	Optional	Skills-to-MCP bridging

MCP servers let AI agents — like Claude Desktop or custom LLM applications — interact with your application through tools, resources, and prompts.

The package wraps the Model Context Protocol as a first-class Oridecon citizen: handlers, transports, and connectors are wired through the DI container with the same provider/module lifecycle as any other extension.

Core Concepts

Server vs Client

Role	Class	Purpose
Server	MCPServer	Exposes tools/resources/prompts to AI clients. JSON-RPC message router.
Client	MCPClient	Connects to external MCP servers (stdio or SSE) and calls their tools.

Tools, Resources, Prompts

An MCP server exposes three capability types:

• Tools — callable functions the AI can invoke (tools/list, tools/call).
• Resources — data the AI can read (resources/list, resources/read).
• Prompts — template-driven messages (prompts/list, prompts/get).

Handlers

Handlers implement the routing for each capability:

Handler	Registers	Dispatches to
ToolHandler	@tool-decorated methods, MCPController subclasses, connectors	MCPToolProviderProtocol
ResourceHandler	@resource-decorated methods, connectors	MCPResourceProviderProtocol
PromptHandler	@prompt-decorated methods	MCPPromptProviderProtocol

Transports

Transport	Direction	I/O
StdioTransport	Server-side	stdin/stdout (local desktop tools)
SSETransport	Server-side	HTTP + Server-Sent Events
StdioClientTransport	Client-side	stdin/stdout subprocess
SSEClientTransport	Client-side	HTTP POST + SSE

Typical Usage

Exposing Tools via MCPController

from oridecon.ai.mcp import MCPController, tool, MCPModule
from oridecon.contracts.data.vector.protocols import VectorStoreProtocol


class SearchController(MCPController):
    def __init__(self, vector_store: VectorStoreProtocol) -> None:
        self._store = vector_store

    @tool("search_docs", description="Search documents by query")
    async def search_docs(self, query: str, limit: int = 10) -> list[dict]:
        result = await self._store.similarity_search(query, k=limit)
        return result.unwrap_or([])


# Wiring
module = MCPModule.configure(controllers=[SearchController])

Auto-Exposing Existing Services

module = MCPModule.from_services(
    services=[UserService, AnalyticsService],
    include_methods=["search", "get_*"],
)

Service methods matching the glob are registered as MCP tools automatically.

Connecting to External MCP Servers (Client)

from oridecon.ai.mcp.client import MCPClientModule, MCPConnection


module = MCPClientModule.configure(
    connections=[
        MCPConnection.stdio(["uvx", "mcp-server-git"], name="git"),
        MCPConnection.sse("http://localhost:9000/mcp", name="local"),
    ],
)

Inject MCPClientRegistry for named access or MCPClient for the primary connection.

Common Patterns

Exposing Connectors Conditionally

ai_mcp:
  connectors:
    filesystem:
      root_dir: "/workspace"        # enables FilesystemConnector
      read_only: true
    web_fetch:
      enabled: true
    github:
      token: "${GITHUB_TOKEN}"      # enables GitHubConnector

Connectors are enabled by supplying the required config keys — absent keys disable the connector.

Custom Tool Provider

from oridecon.contracts.mcp.protocols import MCPToolProviderProtocol


class MyToolProvider:
    async def list_tools(self) -> list[dict[str, Any]]:
        return [{"name": "hello", "description": "Say hello", "inputSchema": {}}]

    async def call_tool(self, name: str, arguments: dict[str, Any]) -> dict[str, Any]:
        return {"content": [{"type": "text", "text": "Hello!"}]}

Register it in a custom provider’s boot() against MCPToolProviderProtocol.

Bridge to oridecon-ai-skills

When oridecon-ai-skills is installed and its provider is registered, SkillToolAdapter is automatically created — all skills become MCP tools.

Best Practices

• ✅ Use MCPModule.from_services() for quick prototyping, MCPController for production.
• ✅ Set read_only: true on connectors that should never mutate state.
• ✅ Set enable_streaming: False in tests to simplify assertions.
• ❌ Don’t import MCPServer from the server module directly — resolve it from the container.
• ❌ Don’t expose database write connectors (sql.read_only: false) to untrusted AI clients.

Next Steps

• How-Tos — specific task recipes
• Configuration — every config key
• Architecture — internal design and extension points