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Centralize prompts, model gateways, RAG, and secrets in a shared AI platform.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Shared Services" yet — see the docs or source repo.
Design an MCP Shared Services based LLM integration plan for our FastAPI team, with centralized model configs, prompt templates, auth, and fallback strategy, and suggest module boundaries.
A shared AI service architecture plan describing module responsibilities, integration flow, and governance.
Using MCP Shared Services, design an enterprise knowledge base QA solution including document ingestion, vector retrieval, prompt registration, model invocation, access control, and API wrapping.
A complete RAG service design covering data flow, component relationships, and security controls.
Create a shared service governance policy for our AI platform, focusing on secret management, tool registry, config center change flow, audit requirements, and multi-environment release strategy.
An actionable governance policy draft for safely and reliably managing shared AI infrastructure.
Expose modular retrieval and reasoning tools to AI assistants through MCP.
Manage multiple MCP servers and load tool schemas on demand efficiently.
Turn unstructured documents into a searchable knowledge base for AI agents.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Safely let LLMs operate Gmail, Drive, and Calendar via MCP.