MCP Dev Summit Bengaluru 2026

MCP standardizes how tools are described and invoked, but not what they should return. That leaves a critical decision open: whether an MCP tool returns raw data, shared state, or decisions. That choice determines where intelligence lives.

This talk presents three placement patterns in MCP tool design. Centralized Intelligence treats tools as data sources, returning raw inputs agents must interpret. Streaming Intelligence exposes pre-processed state so agents operate on shared context. Edge Intelligence pushes decisions into tools, with MCP delivering opinionated outputs rather than raw inputs.

These patterns produce fundamentally different behavior across tool call volume, context window usage, latency, and failure modes. In multi-agent systems they determine whether agents converge or diverge: raw data per agent leads to inconsistent reconstruction; shared state or decisions make coherence a property of the architecture itself.

This talk introduces a practical decision framework for MCP tool design based on data volatility, agent reuse, and consistency requirements.

MCP is a tool contract. Intelligence placement is the architectural decision it doesn't make for you.