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Turn multiple LLMs into MCP tools with JSON, no Python required.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "model-gateway" yet — see the docs or source repo.
Help me create a JSON config for model-gateway that defines GPT, Claude, and Gemini as three MCP tools for summarization, code review, and marketing copy generation. Include the tool name, description, input parameters, and model mapping for each.
A ready-to-use JSON configuration defining three MCP tools powered by different models.
I want to wrap models from different vendors behind one interface. Design an MCP tool configuration for model-gateway so users can switch between GPT-4, Claude, and Gemini with the same input format, and explain how to standardize the parameters.
A configuration approach with a unified input schema plus parameter mapping and switching guidance for each model.
Give me an implementation example using model-gateway: connect document summarization, customer reply drafting, and meeting note organization into an MCP workflow using only JSON configuration, with no Python. Also include deployment and maintenance recommendations.
A no-code configuration example for team workflows, including deployment, maintenance, and usage recommendations.
Route LLM completion requests to OpenAI-compatible providers through MCP tools.
Connect multiple MCP servers through one gateway for unified tool access.
Turn OpenAPI specs into MCP servers for LLM access to REST APIs.
Access multiple LLM providers through one encrypted OpenAI-compatible gateway.
Securely expose OpenAPI endpoints as MCP tools with auth, limits, and auditing.
Generate MCP tools from OpenAPI specs for natural-language API interaction.