Data analysis agents often produce shallow or non-auditable summaries because they lack the explicit reasoning frameworks and validation habits used by domain experts, especially when evidence must be gathered across heterogeneous data sources. We propose structured strategy injection, which injects compact methodology specifications with explicit checkpoints into an agent’s controller context, turning expert workflows into executable plans and enabling compliance-based transparency. To make such methodology-driven planning executable and auditable in heterogeneous settings, we introduce Data2MCP, a router-based orchestration infrastructure that standardizes SQL databases, knowledge graphs, vector stores, and tables as MCP-compatible tools and routes queries dynamically for multi-source retrieval and join-like reasoning. We compare Data2MCP against a RAG baseline and DA-Agent across multi-source retrieval, DAComp, and BIRD MiniDev. In multi-source retrieval, Data2MCP outperforms DA-Agent, and strategy injection benefits both architectures. On DAComp, strategy injection improves DA-Agent from 50.84 to 57.17 (+6.33). The code is available at https://github.com/turambar928/Data2MCP.