【AI開発指南書：第2回】Claude・Cursorと自社DBをつなぐ実践MCPサーバー構築The second installment of a hands-on series explains how to build a custom MCP…

Building AI agents that can safely reach into a company's own data remains one of the harder practical problems in enterprise adoption. The second installment of a Japanese-language hands-on series published on Qiita addresses exactly this challenge, walking readers through how to build a custom Model Context Protocol (MCP) server that connects Claude and Cursor to an in-house database. It matters because most useful agent workflows depend on private, structured data that general-purpose models cannot access on their own.

The series presents itself as a field-oriented design guide spanning three themes: MCP, advanced retrieval-augmented generation (RAG), and autonomous agents. The first installment reportedly dissected the internal structure of the Model Context Protocol, establishing the conceptual groundwork. This second article shifts from theory to implementation, covering the concrete steps required to expose an internal database as a callable tool, together with the design principles for bridging AI agents and existing infrastructure safely.

For readers new to the underlying technology, MCP is an open protocol introduced by Anthropic in late 2024 that standardizes how AI applications connect to external data sources and tools. Rather than writing a bespoke integration for every combination of model and data source, a developer implements an MCP server once, and any MCP-compatible client can discover and invoke the capabilities it exposes. That client-server split is what makes the same database connector usable from both Claude, Anthropic's model family, and Cursor, an AI-first code editor that has adopted the protocol.

On the technical side, an MCP server generally defines a set of tools, which are functions the model may call, and can also serve resources and predefined prompts. When the server is wired to a database, those tools typically wrap SQL statements or an object-relational mapping layer behind a declared schema. Importantly, the model does not touch the database directly. It issues a structured request describing the tool and its arguments, and the server is responsible for validating that request, executing the query, and returning the result in a form the model can reason over. This indirection is the point at which security controls belong.

The article's emphasis on connecting "safely" is consistent with the concerns that tend to dominate real deployments. Common safeguards in this pattern include parameterized queries to prevent injection, read-only or narrowly scoped credentials so an agent cannot mutate or delete records unexpectedly, row- and column-level access control, and human-in-the-loop confirmation before any destructive operation runs. Limiting how much schema and data the model can see also reduces the risk of leaking sensitive information into a prompt or response. While the exact measures the tutorial recommends are not detailed in the excerpt, a build focused on in-house data would likely address several of these dimensions.

The broader context is a rapid consolidation of the industry around MCP as a connection standard. Since its release, the protocol has been picked up beyond Anthropic's own ecosystem, with support appearing across a range of clients and tooling, which reduces the lock-in risk of investing engineering time in a server. That trend makes a locally built connector more durable, because the same server can, in principle, be reused as new compatible clients emerge. It also situates this tutorial alongside adjacent approaches, most notably RAG, which the series covers separately. Where RAG retrieves unstructured text to ground a model's answers, an MCP database tool gives the model a structured, queryable interface, and the two techniques are often complementary rather than mutually exclusive.

For teams evaluating agentic development, the practical takeaway is that the hard work is less about the model and more about the integration boundary: defining clear tool contracts, enforcing permissions, and validating inputs and outputs. A tutorial that starts from a self-hosted MCP server and an existing internal database reflects how many organizations are likely to approach the problem, since it lets them keep data inside their own perimeter while still benefiting from AI clients they already use. Readers should treat the walkthrough as one implementation pattern among several, and adapt the security specifics to their own database engine, compliance requirements, and threat model before putting anything into production.