HasMCP acts as a hosted gateway that converts existing REST APIs into Model Context Protocol (MCP) servers, enabling AI agents and IDEs to interact with external data without requiring manual backend development. By importing OpenAPI or Swagger specifications into the platform, users can instantly expose their endpoints as type-safe tools for environments like Claude, Cursor, and ChatGPT. The platform differentiates itself by focusing on the practical constraints of working with LLMs, particularly token management. Rather than passing raw, bloated API responses directly to an agent, it uses JavaScript-based interceptors and JMESPath filtering to prune payloads down to the essential information. This keeps context windows clean and reduces operational costs. While local MCP setups often require significant boilerplate and server management, this tool moves that logic to a managed dashboard where authentication flows and telemetry are handled out of the box. It serves as a pragmatic solution for teams needing to connect legacy systems to agentic workflows quickly, though it does involve placing a hosted middleman between your private API and your AI client.
Problem: Developers need to use internal company APIs within AI-powered code editors like Cursor or Zed, but building and maintaining custom MCP servers for every endpoint is labor-intensive.
Solution: HasMCP imports OpenAPI or Swagger specifications and automatically generates a hosted MCP server, making internal tools available to the IDE without manual coding.
Example: A developer imports a internal CRM API spec to allow Cursor to fetch customer data and write context-aware emails.
Problem: Standard REST API responses often contain excessive metadata that consumes expensive LLM tokens and clutter the model's context window.
Solution: The platform uses JMESPath and JavaScript interceptors to prune JSON payloads, removing unnecessary fields before the data reaches the AI agent.
Example: Filtering a 50KB product catalog response down to just the SKU and price, reducing token usage by over 90%.
Problem: AI agents often struggle to handle secure authentication flows when accessing protected user data from third-party SaaS platforms.
Solution: HasMCP implements the MCP elicitation flow, which prompts users for credentials and securely manages OAuth2 tokens in an encrypted vault.
Example: An AI assistant requesting access to a user's private GitHub repository triggers a standard login prompt through the HasMCP gateway.
Target audience: Best for: Software engineers building agentic workflows, Teams using AI-integrated IDEs like Cursor or Zed, and SaaS companies providing AI-ready API access.
Pricing: Open Source · Categories: Code Assistants, Developer Tools, Low-code/No-code
Tags: ai agent, API, code assistant, developer tools, low-code/no-code
Visit HasMCP | No-Code API to MCP Server Gateway
HasMCP converts REST APIs into Model Context Protocol tools by importing OpenAPI or Swagger specifications. It allows AI models to interact directly with internal or third-party endpoints. The platform provides payload pruning through JMESPath and JavaScript interceptors to reduce token overhead, dynamic tool discovery for navigating complex API sets, real-time telemetry inspection logs, and managed OAuth2 authentication flows.
HasMCP produces type-safe tools that integrate with AI agents and code editors supporting the Model Context Protocol. Supported clients include developer-focused IDEs like Cursor and Zed, as well as conversational AI environments like Claude and ChatGPT. By exposing your APIs through the gateway, these clients can query your data and execute endpoints using their standard tool calling interfaces.
Standard REST API responses often return bulky JSON payloads with redundant metadata that exhausts context windows. HasMCP addresses this issue by letting developers define JMESPath queries and JavaScript-based interceptors. These filters strip out unnecessary data before payloads reach the LLM, passing only the critical attributes required by the agent and cutting token usage significantly.
HasMCP uses an OAuth2 elicitation flow to manage secure user authentication. When an AI client attempts to call a protected endpoint, the gateway prompts the user for necessary credentials. HasMCP then stores and manages these OAuth2 access tokens inside an encrypted vault, enabling agentic workflows to access secured third-party or internal services safely.
HasMCP is distributed as an open source solution. Developers can use and deploy the platform without paying license fees to access its core gateway capabilities, API specification importing, payload pruning filters, and authentication handling features. You can review setup requirements and deployment instructions directly via the project repository and website.