7 Repos
Validates generated tool call arguments against a JSON Schema during decoding to reject malformed or extra parameters.
Distinct from Schema-Constrained Sampling: Distinct from Schema-Constrained Sampling: focuses on validating tool-specific arguments rather than constraining general token selection to a schema.
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mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and Anthropic-compatible APIs. It serves as a multi-model serving platform, capable of loading several models in a single server process with per-request routing and on-demand loading and unloading. The engine supports multimodal inference, processing text alongside images, video, audio, and speech inputs, and includes a quantized model deployment runtime that reduces memory use and speeds up inference on consumer hardware. The project distinguishes itself through an agentic tool exe
Validates tool call arguments against JSON Schema during decoding to prevent malformed parameters.
CUE is a constraint-based configuration language designed for data validation, schema definition, and code generation. At its core, it unifies types and values into a single concept, enabling compile-time validation that catches structural and value errors before runtime. The language treats data and constraints as the same thing, allowing a single definition to serve as both a schema and concrete configuration data. CUE distinguishes itself through its constraint-based unification engine, which combines multiple configuration sources into a single coherent result by merging their constraints
Uses OpenAPI data schemas directly to validate JSON or YAML data.
Typia is a compile-time code generator that transforms TypeScript type annotations into runtime validation, serialization, and schema functions without requiring decorators or separate schema files. It generates optimized validation and serialization code during TypeScript compilation, producing dedicated functions for each type that eliminate runtime schema objects for faster execution. The project extends this core capability into several integrated areas. It generates fully typed client SDKs from NestJS controller source code, keeping server and client types synchronized automatically. It
Parses and validates tool arguments with type coercion and returns annotated error markers.
PromptX is an LLM agent orchestration framework designed to execute multi-step workflows using autonomous agents. It features a sandboxed tool execution environment for secure filesystem operations and external API integrations, alongside a persona management system that defines professional roles and domain expertise to control agent behavior. The system implements a semantic memory network for persistent knowledge storage, utilizing graph-based memory and engrams to retain information across sessions. This cognitive memory includes specialized tools for knowledge graph visualization, allowi
Enforces strict parameter types and schema validation for external API and function calls before execution.
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
Enforces strict JSON Schema validation for tool arguments during registration.
This is a software development kit for integrating the Model Context Protocol into Java applications. It serves as a framework for building AI servers and communication layers that exchange prompts, resources, and tool definitions between AI clients and servers. The SDK provides a transport-agnostic communication layer, allowing bidirectional data exchange over standard I/O, HTTP, or Server-Sent Events. It includes a generative AI resource manager for exposing structured data and prompt templates, and a standardized interface for implementing protocol clients and servers. The project covers
Validates tool arguments against JSON schemas before routing them to server handlers.
Ollama-mcp-bridge is a middleware service that connects local language models to external tools and data sources. It functions as a bridge, enabling models to execute real-world tasks and access live information by translating natural language prompts into standardized protocol-compliant tool calls. The project distinguishes itself by implementing the Model Context Protocol to facilitate communication between local inference environments and remote service providers. It manages these connections through a centralized registry, allowing for the consistent orchestration of multiple external too
Enforces strict data structure requirements on model-generated tool arguments to ensure compatibility with external interfaces.