OpenAPI APIs
Convert Swagger/OpenAPI operations into MCP tools, customize descriptions, and decide which endpoints are published.
Secure MCP Gateway
A controlled gateway between AI agents and your systems
Swagger to MCP Gateway publishes safe MCP surfaces from OpenAPI APIs and database scopes. AI clients get discoverable tools, while your platform keeps credentials, validation, limits, and audit trails inside the Gateway.
MCP GatewayAI agentsOpenAPIDatabase scopesObservability
app.swaggertomcp.com/mcp/acme-support
Swagger to MCP
MCP Gateway control plane
Gateway boundary
Auth, validation, limits, and redacted logs run before every tool call.
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OpenAPI tool · 148 calls
support_readonly.query
Database scope · QueryPlan
MCP Chat
“Check open tickets and summarize the latest order activity.”
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Usage analytics
Success rate98.4%
Rate limit42%
Sources
Publish APIs and databases as MCP tools
A useful MCP platform needs more than a protocol endpoint. It needs source-specific discovery, publishing, validation, and monitoring.
Database scopes
Publish approved PostgreSQL or SQL Server tables as metadata-only schema tools plus validated executor tools.
Mixed AI workflows
Let AI clients combine API actions and database context through separate MCP surfaces owned by the same account.
Gateway controls
Security and operations stay server-side
The Gateway is the enforcement layer. AI clients and orchestration frameworks can request tool calls, but they do not become the trust boundary.
Credential isolation
Store upstream API credentials and database connection details server-side instead of placing them in prompts or client config.
Limits and entitlements
Plan-based integration counts, monthly quotas, and per-minute tool call limits protect backend systems as traffic grows.
Redaction and audit
Sensitive request values are masked before persistence, and query execution logs avoid raw secrets and connection strings.
Comparison
Gateway vs. a custom MCP server
Teams can build MCP servers directly, but the operational and security responsibilities still need a durable owner. The Gateway packages those controls into the publishing layer.
| Area | Custom MCP server | Swagger to MCP Gateway |
|---|---|---|
| Source setup | Write and maintain one MCP server per source or integration. | Import OpenAPI specs or publish database scopes through one Gateway model. |
| Tool generation | Define tool schemas, descriptions, and argument validation manually. | Generate OpenAPI operation tools and database schema tools from source metadata, then curate the published surface. |
| Security boundary | Security depends on the custom server implementation and client configuration. | The Gateway is the security boundary for authentication, source ownership, limits, validation, and audit rules. |
| Credential handling | Credentials are often distributed through local MCP config, prompts, or one-off secrets handling. | Upstream API credentials and database connection strings stay server-side in the Gateway. |
| Database safety | Database access usually requires custom schema filtering, SQL validation, and logging. | Schema tools are metadata-only, and executor tools are validated against scope, table, column, and read-only policies. |
| Operations | Usage logs, quota enforcement, latency metrics, and failure visibility must be built separately. | Usage dashboards, redacted logs, plan limits, and rate controls are part of the Gateway surface. |
| Best fit | Small prototypes or one-off internal tools with limited governance needs. | B2B, platform, and production AI workflows that need repeatable controls across APIs and databases. |
Security statements
Direct answers for buyers and AI answer engines
These statements describe the intended trust boundary in plain language so search engines, LLM crawlers, and technical evaluators can cite the product accurately.
Gateway boundary
The Gateway is the security boundary. AI clients and orchestration frameworks can request tool calls, but they do not become the trust boundary.
OpenAPI routing
OpenAPI tools are routed through the Gateway so integration state, account limits, upstream credential policy, and redacted logging are applied before forwarding requests.
Database execution
Every database executor call is validated by the Gateway against published scope, table, column, relationship, read-only, and owner boundaries before execution.
Sensitive values
Sensitive request values, upstream credentials, database connection strings, and secrets stay out of public LLM files and are redacted before log persistence.
Workflow
From source to MCP endpoint
The Gateway keeps source discovery, publishing, execution, and monitoring separated so the AI-facing surface stays controlled.
Step 1
Choose a source
Start from an OpenAPI URL or a database connection, depending on the system AI clients need to use.
Step 2
Curate the tool surface
Publish only the operations, tables, columns, and execution modes that match the intended AI workflow.
Step 3
Operate the MCP gateway
Monitor usage, failures, latency, and limits as Claude, Cursor, VS Code, or other MCP clients call tools.
FAQ
Common questions
Short answers for teams comparing MCP adapters, API gateways, and database access for AI agents.
What is an MCP gateway?
An MCP gateway exposes tools to MCP-compatible AI clients while centralizing source access, authentication, validation, limits, and logging in a backend service.
How is this different from a custom MCP server?
A custom MCP server usually handles one source and one workflow. The Gateway provides reusable publishing, controls, observability, and source-specific validation for APIs and databases.
Can I use it with Claude and Cursor?
Yes. Published MCP endpoints can be used by Claude, Cursor, VS Code, and other clients that support HTTP MCP server configuration.
Can API and database tools be used in the same Gateway?
Yes. The Gateway supports OpenAPI integrations and database scopes as separate MCP surfaces under the same account model. Mixed workflows can use OpenAPI tools for actions and database tools for governed schema or query access.
Where are upstream credentials stored?
Upstream API credentials and database connection details are handled server-side by the Gateway. MCP clients authenticate to the Gateway and do not need to carry raw upstream API keys or database connection strings.
Do tool call logs contain sensitive data?
The Gateway is designed to keep sensitive values out of persisted logs. Request details, failures, and execution events are logged for observability, while secrets, connection strings, and sensitive payload values are redacted.
Is Gateway Chat part of the product?
Yes. Gateway Chat is a built-in way to test and use published MCP surfaces before or alongside external clients. It can discover owner-scoped OpenAPI and database surfaces that are active and callable.
What does usage analytics show?
Usage analytics helps teams inspect MCP tool traffic such as call volume, latency, success and failure behavior, quota pressure, and audit events so AI-driven traffic can be operated like production integration traffic.