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OpenAPITools SDK

OpenAPITools SDK

Local
github.com/kvssankarDeveloper Tools1Updated 81w ago

About OpenAPITools SDK

OpenAPITools SDK is a JavaScript package for developers to create and execute AI tools through a unified interface for Claude, GPT models, and LangChain. Tools can be implemented as Python or Bash scripts and run locally.

Key features

  1. 01Create tools with Python or Bash
  2. 02Manage and execute tools through one SDK
  3. 03Connect Claude, GPT, and LangChain
  4. 04Run tool code locally
  5. 05Call tools in API mode

Use cases

  1. 01Build script-backed tools for AI assistants
  2. 02Reuse tools across model frameworks
  3. 03Use Python packages for complex tasks
  4. 04Run Bash tools in Node.js environments

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

OpenAPITools SDK

Introduction

OpenAPITools JS package enables developers to manage, and execute tools across multiple AI API providers. It provides a unified interface for working with tools in Anthropic's Claude, OpenAI's GPT models, and LangChain frameworks.

With OpenAPITools, you can:

  • Create tools as Python or Bash scripts with standardized input/output
  • Access these tools through a single, consistent SDK
  • Integrate tools with Claude, GPT, and LangChain models
  • Build interactive chatbots that can use tools to solve complex tasks
Install from npm
npm install @reacter/openapitools

Tool Execution Details

Python Tools
  • Python tools are executed spawning new threads in nodejs
  • Considerations:
    • Has interpreter startup overhead in JS environments
    • Full privacy (code runs locally)
    • Better for complex tasks requiring Python packages
  • Python tools receive arguments via an input_json dictionary and can access environment variables through input_json["openv"]
Bash Tools
  • Bash tools are executed as subprocesses
  • Arguments are passed as JSON to the script's standard input
  • Very fast execution in nodejs environments
  • Preferred for JavaScript/nodejs environments for better performance
  • Note: Bash tools should be tested in Linux environments or WSL, as they may not function correctly in Windows

Usage Modes

Local Mode (preferred)
const toolsAdapter = new AnthropicAdapter(
    path.join(__dirname, "openapitools"),
    { verbose: true }
  );
API Mode (rate limits apply)
adapter = ToolsAdapter(api_key="your_api_key")

Performance Considerations

  • Python Tools: Better for readability and when complex Python packages are needed
  • Bash Tools: Significantly faster in nodejs environments with minimal overhead
  • For maximum performance in JavaScript/nodejs environments, prefer Bash tools
  • If you need more readability or complex packages, go with Python tools

Security and Privacy

  • All tool execution happens locally within your environment
  • No code is sent to external servers for execution
  • Environment variables can be securely passed to tools

Integration with AI Models

OpenAPITools provides native integration with:

  • Anthropic's Claude
  • OpenAI's GPT models
  • LangChain frameworks

This allows you to build AI assistants that can leverage tools to perform complex tasks.

Note

Platform Considerations: Python servers have better compatibility with the latest AI packages, LangChain SDKs, and other AI development frameworks. Consider working in a Python-centric environment using the Python SDK: pip install reacter-openapitools

Visit docs.openapitools.com for more information on how to use the OpenAPITools SDK, including detailed examples and API references.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.