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The Local-First Spatial Audit & Surgical Revert Layer for Autonomous AI Agents

OpenMetadata context provider for akko-mcp-trino: descriptions, owners, tiers, PII classifications and keys, handed to the agent alongside the columns.

TypeScript LLM router — smart fallbacks, named routes, cost tracking, and caching. Zero runtime dependencies.

A PTY-backed compatibility shim for `claude -p` (Claude Code headless/print mode). Drives the interactive `claude` CLI via node-pty and exposes the same option surface and stream-json contract. Ships as both a library and an `ocp` CLI binary.

Name reserved. The package ships with its first public release.

Knowit Reference Case Model Context Protocol (MCP) Server for Claude Desktop, Cursor, and Windsurf

MCP server: independent, fresh-session adversarial critique of code or docs artifacts, with built-in convergence.

API Авито для ИИ-ассистентов: объявления и статистика, чаты с покупателями, заказы и Авито Доставка, автозагрузка, продвижение, отзывы

[](https://glama.ai/mcp/servers/jgsystemsconsulting/jgs-magic-sysmlv1-mcp) 🐍 🏠 🍎 🪟 🐧 - AI-to-CATIA Magic bridge for live SysML v1 models: 130+ MCP tools to query, audit, trace, and edit blocks, requirements, allocations, and diagrams in CATIA Magic Systems of Systems Architect 2026x over a local, air-gapped connection. FREE tier reads and audits; PR

[](https://glama.ai/mcp/servers/ami-guru/x402-scraper-engine) 📇 ☁️ - Production HTTP 402 micropayment web scraper, Llama-3 digest, and Twitter intelligence engine for AI agents on Base L2. Zero API keys, pay-per-call in USDC.

[](https://glama.ai/mcp/servers/zzzjy765/ottersnap-mcp) 📇 ☁️ - Glint Render (by Moyu) — web rendering & evidence API for AI agents: screenshots, PDFs, OG images, monitoring, extraction and tamper-evident evidence. Free tier included. `npx -y ottersnap-mcp`

[](https://glama.ai/mcp/servers/adiosdotdev/mcp) 📇 ☁️ - Official Adios MCP server for managing workspaces, previews, builds, logs, services, and production deployments.

[](https://glama.ai/mcp/servers/Ilmar7786/marzban-sdk) 📇 ☁️/🏠 - MCP server for the [Gozargah/Marzban](https://github.com/Gozargah/Marzban) proxy/VPN panel API, built on the typed `marzban-sdk` client. 21 tools across users, subscriptions, nodes, hosts, config and system stats, plus 3 investigation prompts. Env-only credentials (never a tool argument), profile-gated tool visibility (`readonly

[](https://glama.ai/mcp/servers/CodesWhat/portkey-admin-mcp) 📇 ☁️ - Portkey Admin API control-plane MCP server with 181 tools across 20 domains, including prompts, configs, keys, analytics, guardrails, integrations, and deployments.

[](https://glama.ai/mcp/servers/abnegate/magents) 🦀 🏠 🍎 🐧 - Shared session bus for Claude Code, Codex, Cursor, Grok, and friends: MCP + CLI to list/read/handoff/send/spawn across live agent sessions. Install: `brew install abnegate/tap/magents`.

[](https://glama.ai/mcp/servers/apatureai/bastion) 📇 🏠 - In-loop design review of rendered UI for coding agents: submit a preview URL, get grounded findings with element references, then recheck the fix. Read-only by design — never edits code. OAuth 2.1 and SSRF-safe URL handling reference implementation. `npx -y @apatureai/bastion`

[](https://glama.ai/mcp/servers/RPER2001/rigshare-mcp) 📇 🏠 ☁️ - Rent GPUs, robots, drones, and construction equipment on RIGShare, and onboard equipment owners; agents can quote, book, run remote sessions, and publish listings.

[](https://glama.ai/mcp/servers/Cubiczan/chp-mcp) 📇 🏠 🍎 🪟 🐧 - CHP Profile B spend/HITL and tool-approval receipts. `npx -y @cubiczan/chp-mcp`

[](https://glama.ai/mcp/servers/nworks3d/THOR-memory-bible-for-AI) 🦀 🏠 🪟 🐧 - Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs; prose-only rules can warn but never block. Append-only event log, per-project scoping, and a health report that names the facts whose anchor

[](https://glama.ai/mcp/servers/setheerwagen/local-ai-mcp-servers) 🐍 🏠 - Manage a local model machine: pull/remove Ollama models, switch the inference service between models, and run LoRA fine-tuning jobs. `pip install mcp-modelmanager`