Swagger to MCP logoSwagger to MCP Gateway
OpenAPI → MCPTool descriptionsTool-level controlObservability

Turn any API into MCP tools — without exposing secrets to AI models

Swagger to MCP Gateway converts your OpenAPI specification into MCP tools that LLM applications can safely use. Publish a secure gateway endpoint, decide which operations become tools, and monitor how AI systems interact with your APIs.

Publish your APISee architectureView pricing →

Outcome

MCP endpoint in minutes

Import OpenAPI, publish a gateway URL, and validate with MCP tool discovery.

Security

Server-side credential isolation

LLM clients authenticate to the gateway; upstream credentials remain protected.

Operations

Visibility from day one

Track usage, failures, and latency trends for AI-driven tool traffic.

Works with

ClaudeCursorVS CodeAny MCP client

Integrating APIs with LLM tools is harder than it should be

Most APIs were built for humans and backend services — not for tool-based AI workflows. Teams end up reinventing adapters, leaking credentials, and losing visibility once agent traffic starts.

Tool compatibility

LLM clients expect MCP-compatible tools, while most APIs only expose REST or GraphQL endpoints.

Security risks

Giving upstream credentials to AI tools increases blast radius and makes access hard to control.

Lack of visibility

Without an operational layer, it’s difficult to understand usage, failures, and performance.

A clean boundary between models and your infrastructure

Clients speak MCP to the gateway. The gateway enforces policy and calls your upstream API using server-side credentials.

Architecture

LLM Client

Claude, Cursor, VS Code, or any MCP-compatible client

↓ MCP (tools/list, tools/call)

Swagger to MCP Gateway

  • Converts OpenAPI → MCP tools
  • Controls tool exposure and auth behavior
  • Stores redacted request trails
  • Provides analytics and monitoring
↓ Upstream HTTP request (server-side auth)

Your API

Existing REST endpoints — no changes required

No secrets in prompts or client configs

Clients authenticate to the gateway. Upstream API credentials stay server-side and never become part of model context.

Tools that models can understand

Each operation becomes a tool with schema and method-level descriptions that improve tool selection and argument correctness.

Control what becomes a tool

Expose safe operations, hide sensitive endpoints, and evolve your tool surface over time without changing your upstream API.

Operate with confidence

Understand usage, failures, and latency trends with redacted request logs and production analytics.

Core capabilities

Everything you need to publish MCP tools from an existing API — without building adapters or exposing credentials.

OpenAPI → MCP tool conversion

Import your OpenAPI specification and automatically generate MCP tools for each API operation. No manual wrappers, no brittle hand-written schemas, and no custom client glue code.

LLM-friendly tool descriptions per method

Add clear guidance for each tool: when to use it, important parameters, expected outputs, and what to avoid. This improves reliability and reduces incorrect tool calls.

Tool-level enable/disable control

Decide exactly which operations are exposed. Keep administrative or destructive endpoints private while making safe operations available. Your MCP server becomes a curated tool catalog, not a raw API mirror.

Authentication controls

Keep authorization behavior centralized at the gateway. Define and enforce how requests are authenticated and which tools can be accessed.

Logging and analytics for AI-driven traffic

Once agents start calling tools, you need visibility. The gateway records activity so you can understand what is happening and why.

Request trails with redaction

Capture request/response trails for debugging and audit while masking sensitive data. This gives teams enough context to investigate issues safely.

Usage and performance monitoring

Track which tools are called, success and failure rates, and response latency. Understand adoption patterns and detect regressions early.

What teams typically track

  • • Tool usage over time
  • • Success vs failure rate
  • • Latency trends
  • • Top tools by volume
  • • Error patterns
  • • Integration health

Why not just build an MCP adapter?

You can — but the hidden work is not the conversion. It’s everything around it: safety, control, and operations.

If you build it yourself

  • • Generate and maintain tool schemas
  • • Create method descriptions that models actually follow
  • • Decide what to expose and keep it in sync with API changes
  • • Handle upstream auth without leaking credentials
  • • Build logging, analytics, and operational dashboards

With Swagger to MCP Gateway

  • • OpenAPI turns into tools automatically
  • • Method-level descriptions live with the tool surface
  • • Tool exposure is controlled and reviewable
  • • Credentials stay server-side behind the gateway
  • • Visibility is built in from day one

How it works

  1. 1. Import your OpenAPI specification

    Provide your OpenAPI URL or upload the spec file.

  2. 2. Generate MCP tools

    Each API operation becomes a structured MCP tool definition.

  3. 3. Configure tool exposure

    Enable only the operations you want AI systems to access.

  4. 4. Publish the MCP endpoint

    Your gateway exposes a single MCP server URL.

  5. 5. Connect LLM clients

    Clients discover and call tools through the MCP protocol.

Scale as your usage grows

Capacity limits are defined by your subscription plan. Start small and upgrade as traffic increases.

Free

$0

  • 1 integrations
  • 1,000 tool calls / month
  • 30 tool calls / minute

These limits are plan-defined capacity (quota + throughput), not per-user custom knobs.

Pro

$19

  • 10 integrations
  • 50,000 tool calls / month
  • 120 tool calls / minute

These limits are plan-defined capacity (quota + throughput), not per-user custom knobs.

Team

$199

  • 50 integrations
  • 200,000 tool calls / month
  • 300 tool calls / minute

These limits are plan-defined capacity (quota + throughput), not per-user custom knobs.

Why capacity limits exist

AI-driven traffic can be bursty. Plan-based capacity ensures consistent performance and predictable scaling as your usage grows.

Build MCP tools from your existing APIs

Start with your OpenAPI specification and publish a secure MCP gateway in minutes.

Publish your API
Secure boundaryCurated toolsMethod descriptionsAnalytics