dbc-mcp-server

DBC-MCP-Server is a Spring Boot service that exposes a Model Context Protocol (MCP) interface for database connection management and AI-assisted database metadata querying. It supports multiple SQL databases (e.g., MySQL, PostgreSQL, SQL Server, SQLite) via dynamic datasource switching and provides MCP “tool functions” for schema/introspection (tables, columns, foreign keys, indexes, engine) plus SQL execution and row count/statistics.

Evaluated Apr 04, 2026 (16d ago)
Repo ↗ Ai Ml mcp spring-boot database metadata schema-introspection ai-tools jdbc postgresql mysql sqlite sql-server
⚙ Agent Friendliness
41
/ 100
Can an agent use this?
🔒 Security
26
/ 100
Is it safe for agents?
⚡ Reliability
16
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
62
Documentation
55
Error Messages
0
Auth Simplicity
35
Rate Limits
10

🔒 Security

TLS Enforcement
20
Auth Strength
20
Scope Granularity
10
Dep. Hygiene
45
Secret Handling
40

Security posture is unclear from the provided README. It includes database credentials in application.properties but does not describe secret management practices (env vars/vault), transport security (TLS) for the MCP SSE endpoint, or authentication/authorization for MCP tools. The presence of an executeSql tool increases risk of SQL injection or unintended data access/modification unless the server enforces strict allowlists/parameterization and least-privilege DB accounts. Dependency versions are listed but no vulnerability/CVE hygiene or security hardening guidance is provided.

⚡ Reliability

Uptime/SLA
0
Version Stability
45
Breaking Changes
0
Error Recovery
20
AF Security Reliability

Best When

You have a controlled environment where an AI agent can connect to known database instances and you want structured schema tooling through MCP for iterative query building.

Avoid When

You cannot restrict/validate tool inputs (especially for executeSql) or you need strong compliance controls not addressed by the provided documentation.

Use Cases

  • AI agents that need to understand database schema (tables/columns/foreign keys/indexes)
  • Natural-language to SQL workflows where an agent fetches metadata then runs queries
  • Cross-database introspection across multiple configured datasources
  • Database-driven code generation workflows (e.g., generate queries or mappings from schema)
  • Operational exploration of table sizes (row counts) and storage engine details

Not For

  • Production environments requiring strong governance/least-privilege controls without additional hardening
  • Use cases that require a public, unauthenticated endpoint (not indicated as such in the README)
  • Workloads that must guarantee safety from arbitrary SQL execution (since an executeSql tool is described)

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Not specified in README
OAuth: No Scopes: No

The README shows only database connection credentials in application.properties and an MCP SSE URL configuration. It does not document any MCP/API authentication mechanism, authorization, or tool-level permissions.

Pricing

Free tier: No
Requires CC: No

No pricing information present (typical for self-hosted open source).

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • executeSql is powerful; agents may attempt data-modifying statements—ensure the tool is constrained/validated in your environment.
  • No documented rate limits; agents may need to self-throttle to avoid exhausting DB connections/limits.
  • No documented auth/authorization; if deployed beyond localhost, add network controls/auth at the MCP layer.
  • Schema tools may require correct datasource selection; ensure the agent knows which datasource nick/name to use (e.g., jys/studyinfo/springai).

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Scores are editorial opinions as of 2026-04-04.

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