OpenTools | The API for LLM tool use

What

OpenTools is an API for LLM tool use that gives developers access to an open ecosystem of MCP tools through a chat completions interface. It is aimed at teams building AI applications that need external actions or data sources, such as web search, real-time location data, or web scraping, without managing separate tool-specific API credentials.

The product appears positioned as an infrastructure layer for developers who want a single, OpenAI-compatible way to connect multiple LLMs with MCP-based tools. Its core workflow is straightforward: send a completion request, specify the model and MCP tool reference, and let the API handle tool execution and unified billing.

Features

• Completions API for tool use — Developers can invoke MCP tools inside a chat completions request, which simplifies adding external capabilities to LLM applications.
• Access to an MCP tool registry — The platform provides a registry of MCP servers for capabilities like web search, location data, and web scraping, reducing the need to source and wire up tools independently.
• Support for multiple LLMs — Applications can use different LLMs through the same API, which helps teams reduce dependency on a single model provider.
• OpenAI-compatible interface — Compatibility with OpenAI-style APIs and traditional function calling can lower migration effort for existing AI applications.
• No personal API keys for listed tools — OpenTools states that personal API keys are not needed for the supported MCP tools, which may reduce setup complexity and credential sprawl.
• Unified billing with at-cost tokens — The product combines tool execution billing and token charges in one place, which can simplify operational and vendor management workflows.

Helpful Tips

• Validate model and tool coverage early — Before adopting, confirm that the specific LLMs and MCP servers your workflow depends on are supported in the current registry.
• Test failure handling across models — Since multi-LLM flexibility is part of the value proposition, evaluate fallback behavior, response consistency, and tool-calling reliability under outage scenarios.
• Review billing assumptions in detail — “At-cost” token pricing and tool-execution charges should be compared against your current stack to understand the real operational impact.
• Use compatibility to reduce migration risk — If your team already uses OpenAI-style chat completions or function calling, pilot OpenTools in a narrow workflow first to measure integration effort.
• Clarify custom MCP support — The page suggests questions around adding MCP servers, but does not confirm the process here, so teams with proprietary tools should verify extensibility directly.