13 Repos
Mechanisms for runtime introspection of external tool capabilities, schemas, and metadata.
Distinguishing note: Focuses on the discovery and schema-parsing of external tools, distinct from the connectivity layer.
Explore 13 awesome GitHub repositories matching artificial intelligence & ml · Dynamic Tool Discovery. Refine with filters or upvote what's useful.
DevToys is a cross-platform desktop application that functions as a comprehensive suite of offline utilities for common software development tasks. It provides a unified interface for performing data formatting, encoding, validation, and asset generation locally without requiring an internet connection. The application is built on a plugin-based extensibility framework that allows users to integrate custom utility modules to meet specific technical requirements. A core differentiator is its clipboard-aware management system, which monitors clipboard content to automatically suggest or open th
Provides a dynamic mechanism for discovering and registering utility modules at runtime.
Composio is an integration platform designed to connect autonomous agents with external software services and APIs. It functions as a tool orchestration framework and a middleware hub, providing a unified interface for managing the lifecycle, authentication, and execution of external tool definitions within agentic workflows. The platform distinguishes itself by utilizing the Model Context Protocol to standardize communication between artificial intelligence models and external data sources. It employs a provider-agnostic adapter pattern to decouple core logic from specific model providers an
Fetches and registers available tool definitions dynamically at runtime to ensure agents always have access to the latest service capabilities.
FastMCP is a Python framework designed for building servers that expose functions, resources, and prompts to AI models using the Model Context Protocol. It simplifies the development process by automatically deriving tool metadata, input schemas, and documentation directly from Python function signatures and type hints. The framework provides a unified container for managing these components, allowing developers to build modular applications that integrate seamlessly with AI assistants. The project distinguishes itself through its support for interactive, server-defined user interface compone
Supports runtime introspection of tool capabilities, allowing models to discover and inspect tools on demand.
This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I
Provides mechanisms for runtime introspection and parsing of external tool schemas to enable dynamic agent capabilities.
This project provides a Model Context Protocol server that enables autonomous agents to interact with and manage automation workflows. It functions as an integration layer, allowing language models to discover, build, test, and deploy complex automation sequences through natural language instructions and structured schema-based communication. The platform distinguishes itself by offering granular control over automation logic, including the ability to perform surgical, incremental patches to specific workflow nodes rather than replacing entire structures. It supports multi-instance connectivi
Enables runtime introspection of tool capabilities and schemas for autonomous agent discovery.
This tool functions as a generator that maps dynamic framework methods and database model properties to static files, ensuring integrated development environments recognize runtime features. It acts as a static analysis helper by inspecting framework structures to provide accurate type hinting and autocompletion for core classes and container-bound objects. The project distinguishes itself by its ability to interrogate the dependency injection registry and scan runtime method registrations to document dynamically added functionality. It further differentiates by performing reflection-based in
Identifies and documents dynamically registered methods to ensure they are recognized by development tools.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Dynamically discovers and loads tool schemas on demand to minimize prompt noise and token usage.
This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu
Loads tool definitions on demand via search to optimize token usage and increase model accuracy.
The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang
Provides mechanisms for runtime introspection of external tool capabilities, schemas, and metadata via the protocol.
ServiceStack ist ein hochperformantes .NET-Webframework für den Bau typsicherer APIs unter Verwendung stark typisierter Request- und Response-Objekte. Es fungiert als nachrichtenbasierte API-Engine, die Geschäftslogik von der Transportschicht entkoppelt, wodurch Services über mehrere Protokolle wie HTTP, gRPC und verschiedene Message-Queue-Provider bereitgestellt werden können. Das Framework zeichnet sich durch seinen typsicheren API-Generator aus, der native Client-SDKs und Data Transfer Objects (DTOs) aus Service-Metadaten über mehrere Sprachen hinweg produziert. Es enthält zudem ein verteiltes Service-Gateway für Microservices-Orchestration, ein Code-First-ORM zur direkten Übersetzung von C#-Objekten in Datenbankdatensätze sowie ein zentralisiertes Identitäts- und Zugriffsmanagementsystem für sicheren tokenbasierten Zugriff. Die breitere Funktionspalette deckt asynchrones Messaging und Echtzeit-Event-Streaming durch Pub-Sub und Server-Sent Events ab. Es bietet umfassende Unterstützung für Datenserialisierung in Formaten wie JSON, XML, ProtoBuf und MessagePack, neben integrierten Authentifizierungs-Flows wie JWT, API-Keys und Step-up-Authentifizierung. Zusätzliches Tooling umfasst automatisierte CRUD-API-Generierung, Hintergrund-Job-Ausführung und Vorlagen für administrative Dashboards.
Dynamically discovers and wires service types by scanning multiple external .NET assemblies at startup.
The Model Context Protocol C# SDK is a library for building clients and servers that implement the Model Context Protocol to integrate AI tools and resources. It provides an AI tool integration framework and a multi-modal content handler to exchange text, images, and binary resources between AI models and external context providers. The SDK utilizes a JSON-RPC communication library to manage bidirectional data exchange. It features a transport-agnostic communication layer that supports standard input and output, HTTP, and in-memory pipes, with specific integration for ASP.NET Core hosting. T
Uses assembly scanning and custom markers to automatically register server tools and resources at runtime.
mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for
Fetches available tools and their schemas from remote servers in real-time without manual configuration.
This project is a Model Context Protocol server that bridges artificial intelligence agents with cloud-based web scraping and automation resources. It functions as a remote task orchestrator, allowing agents to discover, configure, and execute complex browser automation jobs as callable functions within their native environments. The server distinguishes itself by providing a unified framework for managing distributed workflows, including the ability to handle asynchronous task polling, structured data serialization, and real-time status tracking. It supports advanced agentic capabilities suc
Exposes automation capabilities to agents by dynamically serving structured metadata and input requirements for available remote tasks.