API to MCP

The Manager MCP for AI Agents allows users to create, update, test, deploy, and inspect MCP servers directly from their AI coding agent's chat interface. Once the API to MCP manager server (at https://mcp.apitomcp.io/) is connected to the agent and a scoped manager token is created, developers can describe the API they want to convert in natural language. For example, a prompt like "Create an MCP server for our internal support platform using OAuth Authorization Code so each employee connects their own account. Add ticket lookup and workflow tools, test them, then return the MCP URL." The agent then handles the entire setup autonomously. This approach is ideal for teams that want to iterate rapidly without leaving their IDE. The agent can also update existing servers, add new tools, modify authentication, and run tests—all from chat. This two-lane approach—Visual Builder for detailed control and Agent Builder for speed—ensures that both manual and automated workflows are fully supported.

API to MCP provides a comprehensive set of authentication options that mirror real-world API requirements. It supports no-auth for public endpoints, API keys sent in headers or query parameters, Bearer tokens that can be stored or forwarded, Basic Auth for legacy systems, OAuth Client Credentials for server-to-server machine-to-machine communication, and OAuth Authorization Code for per-user account linking. All stored credentials—API keys, Bearer tokens, Basic passwords, OAuth client secrets, access tokens, and refresh tokens—are encrypted at rest and masked in the user interface. Snapshots never include live secrets or active connection tokens. For the MCP connection itself, users can choose from three access modes: Open (public or agent-supplied credentials), OAuth/Bearer Token (client authenticates the MCP connection), or Client Token (an additional API to MCP access layer). This separation of upstream and MCP authentication allows fine-grained control over who can call which tools, making it suitable for enterprise security requirements.

The workflow of API to MCP is designed to be straightforward, with two parallel lanes. Lane A is the Visual Builder, a step-by-step dashboard where you configure the API access (base URL, auth), define tools with input schemas, run tests, optionally apply JMESPath response mapping, and then deploy to a hosted endpoint. Lane B is the Agent Builder, where you connect the manager server once and then instruct your AI agent to create, test, and deploy servers via chat. Both lanes produce a hosted Streamable HTTP MCP URL that is immediately usable by any remote MCP client. The platform handles the runtime—SSL, hosting, usage tracking—so developers focus only on tool definitions and authentication. The resulting MCP server can be configured with either Open or Authenticated access, and it can be shared as a template or published to a public directory. This two-lane approach ensures that both manual and autonomous workflows are fully supported.

Concrete use cases include exposing a company's CRM, ERP, or HRIS to employees through controlled MCP tools, allowing AI agents to look up customer data, sales pipeline, or employee records in natural language. Marketing teams can connect Meta Ads, Google Ads, Google Analytics, and Search Console to build MCP servers that report performance, inspect accounts, and optimize campaigns. E-commerce platforms can create MCP tools for PayPal, Shopify, or WooCommerce to check billing, catalog, and order status. Developer teams can give coding agents access to GitHub, GitLab, Vercel, and Sentry for repository management, deploys, issue tracking, and observability. Public data providers can publish no-auth MCP servers for open APIs like Weather, REST Countries, World Bank, or Hacker News. Content teams can integrate WordPress.com, Contentful, Webflow, or Notion to enable publishing and editorial workflows through AI agents. In each scenario, the outcome is a production-grade MCP endpoint that works with ChatGPT, Claude, Codex, Cursor, Claude Code, VS Code, and custom agents.

API to MCP targets developers, data engineers, platform teams, and anyone building with AI coding agents like Codex, Cursor, Claude Code, or ChatGPT. It is also ideal for SaaS providers wanting to offer MCP integrations, internal tool builders connecting business systems to AI, and teams adopting agent-driven workflows. The platform is compatible with all major MCP clients and requires no custom runtime code. Pricing includes a free tier (no credit card required) and a hosted runtime with SSL and usage tracking. The overall takeaway is that API to MCP removes the engineering friction between APIs and AI agents, enabling organizations to quickly and securely extend their data and actions into the agent ecosystem. By providing both a visual dashboard and an autonomous agent builder, it caters to different working styles and speeds up the journey from API endpoint to actionable MCP tool.