elbi vs python-sdk
elbi: elbi enables developers to define data derivations (transforms, aggregates, models) in Python, serving them as versioned, cached, and governed tools to AI agents via the Model Context Protocol. It ensures agents interact with reliable, verified data outputs, supporting data quality, lineage, and observability.; python-sdk: The MCP Python SDK provides a robust implementation of the Model Context Protocol, enabling developers to build secure, standardized servers that expose data and functionality to LLM applications. It also includes a client library for connecting to any MCP server, supporting various standard transports like stdio, Streamable HTTP, and SSE.
TL;DR
Provide AI agents with governed, reliable data outputs instead of direct raw table access.
- You want the official, MIT-licensed reference Python SDK maintained directly by the Model Context Protocol project.
- You need full low-level control over MCP primitives (resources, prompts, tools, sampling) and standard transports (stdio, SSE, Streamable HTTP).
- You want higher-level conveniences like automatic client composition or less decorator boilerplate; FastMCP wraps the protocol with extra ergonomics.
- You are exposing an existing GraphQL or FastAPI backend where a gateway or adapter (Grafbase or FastAPI-MCP) requires no manual tool code.
Side-by-Side Comparison
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