context-engineering
A training/repository (Python) showing how to build semantic long-term memory for AI assistants using Model Context Protocol (MCP) with FastAPI/FastMCP and a hybrid RAG architecture (vector + graph + scratchpad), including example labs (e.g., a hello MCP lab) and a flagship teaching app (WARNERCO Schematica).
Score Breakdown
⚙ Agent Friendliness
🔒 Security
The README does not describe authentication/authorization, TLS requirements, secret management practices, or rate limiting. It references local server runs and Azure-related components, but provides no documented security controls or operational hardening guidance in the provided content.
⚡ Reliability
Best When
You want a learning-focused scaffold or baseline implementation for MCP tool servers and hybrid retrieval/memory pipelines in Python.
Avoid When
You need a clearly documented, versioned, production SaaS interface with stable guarantees, explicit SLAs, and formal security/compliance statements from the package maintainers.
Use Cases
- • Educational materials and reference implementation for MCP-based AI context/memory systems
- • Building hybrid retrieval pipelines (vector + knowledge graph) for assistant memory
- • Learning/implementing FastAPI + FastMCP tool/resource patterns
- • Prototyping semantic memory stores (JSON/Chroma/Azure AI Search/graph) and session scratchpad behaviors
- • Integrating with MCP clients such as Claude Desktop/Claude Code and debugging via MCP Inspector
Not For
- • A fully managed hosted API/service for production use without adapting the code
- • Turnkey “plug-and-play” memory with guaranteed operational semantics, SLAs, or enterprise support
- • Compliance-sensitive deployments where you require clearly documented security controls and data-handling guarantees from the package itself
Interface
Authentication
The provided README focuses on running local servers (uvicorn + an MCP stdio server) and does not describe authentication, authorization, or scopes for the HTTP/MCP endpoints.
Pricing
No pricing model described; appears to be an open-source educational repository. Running it may incur infrastructure/model/vector-store costs depending on the environment (not specified here).
Agent Metadata
Known Gotchas
- ⚠ Repository appears educational; production-grade behaviors (structured errors, retries, idempotency, rate limits, and auth) are not evidenced in the provided README.
- ⚠ If MCP tools are stateful (scratchpad/session memory), agents may need to manage session identifiers/contexts carefully (details not provided in README).
- ⚠ Hybrid memory configuration may vary (vector/graph/Azure); misconfiguration could lead to partial retrieval or inconsistent behavior.
Alternatives
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Scores are editorial opinions as of 2026-03-30.