OpenAPI operation tools
Import a Swagger/OpenAPI URL, generate MCP tools from operations, rewrite descriptions for agents, and hide endpoints that should not be callable.
Secure MCP Gateway
Publish APIs and databases as MCP tools for AI agents
Swagger to MCP Gateway is the AI-facing publishing layer between MCP clients and your systems. Import an OpenAPI spec or connect a database, curate the tools agents may use, test the behavior in Gateway Chat, then expose a Gateway-routed MCP URL with server-side credentials, validation, limits, and redacted logs.
No credit card required.
No-code MCP publishingOpenAPI to MCPDatabase to MCPGateway ChatRedacted logs
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
One product model for API tools and database tools
The source can be an OpenAPI document or a database connection. The output is the same operational concept: a published MCP surface that AI clients can discover and call through the Gateway.
Database schema and executor tools
Publish PostgreSQL or SQL Server scopes as metadata-only schema tools plus one validated executor mode: QueryPlan or SQL mode.
Same MCP client contract
Claude, Cursor, VS Code, Gateway Chat, and other MCP clients use a Gateway MCP URL and token instead of raw upstream credentials.
Workflow
A publish loop built for real agent testing
The Gateway is not just a static MCP endpoint. It gives teams a repeatable loop for publishing, testing, tightening, and operating an AI-facing tool surface.
Import or connect
Start from an OpenAPI URL or a PostgreSQL/SQL Server database connection. The Gateway discovers operations or metadata before anything is exposed to MCP clients.
Curate for the model
Keep only the tools that match the workflow. Descriptions, table meaning, column rules, and visibility controls help the model choose correctly.
Validate in Gateway Chat
Ask realistic questions, inspect which tools are selected, and adjust descriptions or permissions before adding external MCP clients.
Gateway controls
Security and operations stay server-side
Tool descriptions and prompts help the model choose, but they are not the enforcement layer. The Gateway keeps credentials, validation, limits, and audit controls in backend code.
Credential isolation
Store upstream API credentials and database connection details server-side instead of placing them in prompts, screenshots, local MCP config, or public docs.
Validation before execution
OpenAPI calls pass through publication and plan checks. Database execution passes scope, table, column, relationship, limit, and read-only validation.
Redacted observability
Track usage, latency, success, failures, quota pressure, and audit events while sensitive values are masked before persistence.
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 PostgreSQL/SQL Server 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 before clients see it. |
| Agent testing | Test behavior in whichever MCP client is being configured, often after local setup is already done. | Use Gateway Chat to test tool choice, arguments, database schema grounding, and execution behavior before external rollout. |
| Security boundary | Security depends on the custom server implementation and client configuration. | The Gateway is the backend 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, rate controls, and tool-level metrics 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 backend security boundary. AI clients and orchestration frameworks can request tool calls, but they do not become the trusted enforcement layer.
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.
Gateway Chat
Gateway Chat uses published OpenAPI integrations and database scopes through the same Gateway-controlled MCP path used by external clients.
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 when the agent needs actions, or a database connection when the agent needs governed data context.
Step 2
Curate the tool surface
Publish only the operations, tables, columns, and execution modes that match the intended AI workflow.
Step 3
Test in Gateway Chat
Ask real questions, verify tool choice and arguments, and tighten descriptions or permissions before external client rollout.
Step 4
Connect MCP clients
Use the Gateway MCP URL and token from Claude, Cursor, VS Code, or another HTTP MCP-compatible client.
Step 5
Operate the Gateway
Monitor usage, failures, latency, quota pressure, and redacted logs as real MCP traffic reaches backend systems.
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 instead of spreading those controls across prompts and local client config.
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.
Is this a replacement for an API gateway?
No. Swagger to MCP Gateway is the AI-facing MCP publishing layer. Existing API gateways can still handle normal HTTP traffic, upstream routing, and service-level policy. The MCP Gateway focuses on model-facing tools, descriptions, publication state, MCP tokens, validation, and AI tool observability.
Does it require custom MCP server code?
For common OpenAPI and Database to MCP workflows, no custom MCP server code is required. You import or connect the source, curate the published surface, and expose the Gateway MCP URL to compatible clients.
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.
How do I know whether an agent will choose the right tool?
Start with a small published catalog, write precise agent-ready descriptions, and test realistic questions in Gateway Chat. If the model chooses the wrong tool, improve descriptions, hide confusing tools, or split the workflow into a narrower surface.
Do database schema tools return data rows?
No. Database schema tools are metadata-only. Row data can only come from validated executor tools such as QueryPlan or SQL mode, and those calls are checked against scope, table, column, relationship, limit, and policy rules before execution.
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.