"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.

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.

An MCP server for the Cortex observable analysis and active response engine. It enables LLMs to automate security investigations by running analyzers on observables like IPs and URLs and executing automated response actions.

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.

Behaviorlock is a deterministic compatibility gate for observable AI-agent behavior, enabling comparison of baseline and candidate traces to enforce declared behavior contracts.

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.

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

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.