universal-tool-calling-protocol/utcp-specification
The specification for the Universal Tool Calling Protocol
What it solves
UTCP provides a standardized way for AI systems to discover and interact with tools from different providers. It eliminates the need for custom integrations for every tool, regardless of whether the tool is delivered via HTTP, WebSocket, CLI, or other protocols.
How it works
The protocol defines a common set of formats for tool discovery, call templates, and response processing. It uses a plugin-based architecture that allows it to support multiple communication protocols (such as HTTP, CLI, and MCP) and various authentication methods (API keys, OAuth) through modular components.
Who it’s for
- AI System Developers: Those building agents or LLM-based applications that need to integrate with a wide variety of external tools.
- Tool Providers: Developers creating services or functions that want to make their tools discoverable and callable by AI systems using a standard protocol.
Highlights
- Multi-Protocol Support: Works across HTTP, CLI, WebSocket, Text, and MCP protocols.
- Plugin Architecture: Core functionality is split into pluggable components for high modularity.
- Async/Await Support: Features a full asynchronous client interface for better performance.
- Robust Validation: Uses Pydantic models for comprehensive data validation.
- Flexible Authentication: Supports API keys, OAuth, and custom authentication methods.
Related
- Project
agent-network-protocol/AgentNetworkProtocolANP is an open‑source protocol suite that defines identity, discovery, negotiation, and secure messaging for autonomous AI agents, built on existing web standards (HTTP, DNS, TLS, W3C DID). The current stable spec set is version 1.2, with reference SDKs in the AgentConnect repo.
- Project
apify/mcpcmcpc is a universal command‑line client for the Model Context Protocol (MCP). It maps every MCP operation—tools, prompts, resources, tasks, skills, OAuth authentication, and even experimental x402 payments—into intuitive shell commands. Designed for AI agents and developers, it lets you inspect, debug, and script MCP workflows using standard Unix tools (e.g., jq, xargs). Features include persistent named sessions, progressive tool discovery via `grep`, JSON‑only output mode, secure OAuth 2.1 storage (OS keychain or protected file), AI sandboxing, and lightweight cross‑platform support.
- Project
modelcontextprotocol/modelcontextprotocolModel Context Protocol (MCP) is an open standard that provides a universal way for AI models to connect to data sources and tools, reducing the need for custom integrations.
- Project
mims-harvard/ToolUniverseToolUniverse is an open‑source platform that lets any LLM act as an AI scientist. It provides a standardized protocol, a catalog of 1 000+ scientific tools, a local MCP server, a CLI (`tu`), and a Python SDK for discovering, composing, and running tools—enabling agents like TxAgent and Medea to perform drug‑discovery and omics analyses.
- Project
webmachinelearning/prompt-apiThe Prompt API is a proposed web platform standard that provides a JavaScript interface for web developers to directly prompt language models built into browsers or operating systems, without cloud APIs or bundling models. It offers local, private, offline-capable AI features with simple prompt/promptStreaming methods, system and n-shot prompts, and tool-use support.