"Observable" matching MCP servers:

Enables AI assistants to inspect values, view errors, and capture canvas output from Observable Notebook Kit notebooks running in a web browser.

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 production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.

A production-ready MCP server template with OAuth 2.1, RBAC, and audit logging for building secure, observable tool servers.

Enables orchestration of MCP tool calls through declarative YAML-defined directed graphs with data transformation, conditional routing, and observable execution flows.

An MCP server that checks whether the world changed after a tool call by declaring observable outcomes, verifying them with read-only verifiers, and keeping hash-chained receipts.

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.

Enables autonomous software development by managing requirements, architecture, task dependency DAGs, isolated worker workspaces, execution, contract and integration verification, and checkpoints. It provides observable evidence gates so AI workers can build and verify software against reproducible contracts.

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.

Gives AI agents eyes on in-memory image buffers in live gdb/lldb C/C++ debug sessions: list observable symbols at a breakpoint, view renderings, read exact pixel values, and dump lossless .npy copies. Agents can also drive the human's viewer window (pan, zoom, channels, auto-contrast).

Enables MCP clients such as OpenCode, Claude Code, and Codex to control a real VS Code editor by opening files, visibly typing code, running terminal commands, reading diagnostics, and fixing errors through the VS Code API. It makes AI programming observable by applying real progressive edits and actions that are visible on screen.

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.

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.