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📖 The AI Tool Bible

Google Agent Development Kit (ADK) vs MCP Registry

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
Google Agent Development Kit (ADK)
Agents
MCP Registry
Agents
TaglineGoogle's open-source framework for building, evaluating, and deploying production AI agentsOfficial discovery registry for Model Context Protocol servers, with a browsable UI and a public API.
CategoryAgentsAgents
PricingFree· Framework itself is free and open-source (Apache 2.0). Costs come from the underlying model provider (e.g. Gemini API / Vertex AI usage) and any hosting infrastructure (Cloud Run, GKE, Agent Engine).Free· Free and open source
ModelGemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM—
Editorial score—6.6 / 10
Use cases
Multi-agent research assistantCustomer support triage agentRAG chatbot backed by Vertex AI SearchCode review and refactoring agentBigQuery natural-language analytics agentDocument processing pipelineVoice/streaming conversational agentInternal tool-use agent orchestrating APIsEvaluation and regression testing of LLM workflowsEnterprise workflow automation on Agent Engine
mcp-server-discoveryagent-toolingclient-integrationsregistry-mirror
Pros
  • Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
  • Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
  • Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
  • Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly
  • Rich multi-agent primitives out of the box: SequentialAgent, ParallelAgent, LoopAgent, and hierarchical sub-agents
  • Tight Google Cloud integration for deployment (Cloud Run, GKE, Agent Engine) plus native BigQuery/Vertex Search tools
  • Evaluation harness with trajectory-level scoring is included, not a separate paid add-on
  • First-class MCP (Model Context Protocol) client and server support
  • Official, canonical source for MCP server metadata
  • Open source with a public REST API for programmatic access
  • Supports multiple environments (production, staging, local)
  • Free to use and free to publish servers
Cons
  • Documentation and examples lean heavily on Gemini + Google Cloud; non-Google paths work but feel like second-class citizens
  • API surface is still evolving — breaking changes between minor versions have been common through 2025-2026
  • Multi-agent orchestration primitives are powerful but the graph/callback model has a real learning curve compared to a plain prompt loop
  • Agent Engine deployment is convenient but locks you into GCP billing and quotas
  • TypeScript/Kotlin support lags the Python SDK in features and community examples
  • Bare-bones UI focused on discovery, not curation or reviews
  • Quality and maintenance varies widely across listed servers
  • Aimed at developers; not useful for non-technical end users
Websitegoogle.github.ioregistry.modelcontextprotocol.io
Pick Google Agent Development Kit (ADK) if
  • ✅ Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
  • ✅ Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
  • ✅ Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
  • ✅ Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly
Pick MCP Registry if
  • ✅ Official, canonical source for MCP server metadata
  • ✅ Open source with a public REST API for programmatic access
  • ✅ Supports multiple environments (production, staging, local)
  • ✅ Free to use and free to publish servers
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