"Observable" matching MCP servers:

Enables AI assistants to understand lab topologies, follow operational principles, and interact with real network devices through observable terminals while keeping private access information hidden.

A production-ready starting point for MCP servers, adding per-tool timeouts, concurrency ceilings, error boundaries, secret redaction, correlation IDs, and structured logging so tools are safe and observable for LLM callers.

Provides AI agents with D3.js API documentation and example code from d3js.org and Observable gallery, enabling module discovery, documentation search, and example extraction.

A local, auditable multi-model workflow engine that lets you define YAML graphs for orchestrating LLM agents across vendors, with MCP tools for validation, dry-runs, execution, and human approval, all fully observable in a local web interface.

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

An MCP bridge that lets Codex delegate long-running agent work to the Antigravity CLI, providing observable and resumable tool-based execution with project scoping.

Enables safe, observable Blender research by exposing structured tools for viewport capture, object and material edits, semantic scene creation, and managed .blend project lifecycles with transactional rollback and validation.

Exposes Klipper and Moonraker capabilities to LLM clients through a typed MCP API of tools, resources, and prompts, enabling offline printer.cfg/include validation, AST-based extras development, assisted log and telemetry diagnostics, and controlled G-code execution with safeguards for dangerous commands. It also provides caching, coalescing, circuit breaking, and token-economy optimizations for resilient, observable operation of the printer stack.

A production-grade MCP server designed for multi-tenant, authenticated, and observable AI agent systems, enabling secure tool execution across heterogeneous data sources.

A pluggable, observable modular RAG framework that exposes query knowledge hub, list collections, and get document summary tools via MCP, enabling AI assistants to perform hybrid search and document retrieval with reranking.

A pluggable, observable modular RAG service framework that exposes tool interfaces via the MCP protocol, enabling AI assistants like Copilot and Claude to directly invoke knowledge retrieval and reasoning capabilities.

A pluggable, observable modular RAG service framework that exposes tool interfaces via the MCP protocol, enabling AI assistants like Copilot and Claude to directly query knowledge bases.

A pluggable, observable modular RAG framework exposing tools via MCP protocol for AI assistants like Copilot/Claude to query knowledge bases, list collections, and retrieve document summaries.

A pluggable and observable Retrieval-Augmented Generation framework that exposes hybrid search and document management tools via the Model Context Protocol. It features a complete ingestion pipeline with multi-modal support, automated evaluation using Ragas, and a Streamlit dashboard for real-time tracking.

A local-first job broker with MCP and HTTP interfaces for orchestrating AI work, with cost-aware routing, observable state transitions, and human control.

An observable autonomy harness for AI coding agents that enables long task execution without drift or cheating, while recording all actions for cost and quality steering.

A pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.

An enterprise-grade MCP gateway and security router that gives AI agents secure, observable access to internal corporate knowledge through hybrid vector+BM25 retrieval, RBAC, and prompt injection protection.

A controllable, observable MCP server for dynamically setting tools and intercepting calls in real time, useful as a mock/MITM harness for testing MCP clients.

Aggregates multiple shared MCP servers into a single HTTP gateway, providing a unified, observable, and reusable MCP access layer for multiple AI clients like Codex, OpenCode, and OpenClaw. It centralizes configuration management, logging, health checks, and circuit breaking to simplify multi-client MCP deployments.