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agentify

Agentify is an open-source command-line tool developed by MonadWorks for compiling OpenAPI/Swagger specifications into multiple interface files for AI agents. Starting from a single OpenAPI specification, it can generate up to 9 output formats, including MCP server, CLAUDE.md, AGENTS.md, .cursorrules, Skills, llms.txt, GEMINI.md, A2A, and CLI. The tool parses the specification into an intermediate representation, AgentifyIR, then generates artifacts in each format through pluggable emitters, and performs security scanning (input sanitization, prompt injection pattern detection, generated code scanning) before writing to disk. It is suitable for developers who want their APIs to be callable by AI agents, supports Swagger 2.0 and OpenAPI 3.x, and can handle APIs ranging from small (13 endpoints) to very large (1000+ endpoints).

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About this tool

Agentify (repository name MonadWorks/agentify) is an "Agent Interface Compiler." Its core purpose is to convert existing OpenAPI specifications into multiple files that AI agents can understand and call, so that an API can be recognized and used by agents such as Claude Code, Cursor, Codex, and Copilot, as well as MCP tool callers.

Main capabilities:

  • Generate up to 9 output formats from a single OpenAPI specification: MCP Server, CLAUDE.md, AGENTS.md, .cursorrules, Skills, llms.txt, GEMINI.md, A2A, CLI.
  • Supports Swagger 2.0 and OpenAPI 3.x specifications.
  • Handles APIs of different scales: it is officially said to have been tested from applications with 13 endpoints to platforms with 1000+ endpoints; the GitHub REST API (43 domains, 1093 endpoints) can generate a usable server containing 1093 tools.
  • For specs that do not conform to the specification (such as httpbin), it issues a warning and automatically normalizes them instead of directly rejecting them.
  • Built-in security mechanisms: input sanitization (blocks eval, exec, and Function constructor injection), prompt injection pattern detection, and generated code scanning; every generated artifact undergoes security scanning before being written to disk.
  • Has a capability graph and semantic grouping.
  • Supports custom emitter plugins: a new emitter only needs to implement a simple interface to be integrated.

Usage: Official materials only state that "One command" can generate it, and provide example output, but do not provide specific installation commands, CLI parameters, or running examples. Therefore, installation commands and specific usage steps are not yet provided.

Applicable scenarios:

  • Developers who want their own APIs to be called by AI agents (such as Claude Code, Cursor, Codex, and Copilot).
  • Teams or individuals who need to generate MCP servers for APIs.
  • Scenarios where multiple agent configuration files (CLAUDE.md, AGENTS.md, .cursorrules, etc.) need to be maintained simultaneously and kept in sync with the API specification.
  • Scenarios where tool interfaces need to be automatically generated for large APIs (hundreds to thousands of endpoints).

Limitations and current status:

  • The project's official description says, "This is early," indicating it is at an early stage.
  • The materials do not mention fees, pricing, platform limitations, or performance guarantees.
  • Detailed installation, configuration, and usage steps are not provided, and missing facts are not yet provided.
  • The official project welcomes issue submissions for feedback, and also welcomes contributions of new emitters.

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