"Gcore" matching MCP servers:

A self-hostable, multi-MCP personal-assistant framework with multi-provider LLM orchestration, a real safety model, and a production-grade test suite. Nexus Core is the engine behind a Telegram-based personal assistant: it takes a message (text or voice), routes it through an LLM, lets the model call tools exposed by a fleet of Model Context Protocol servers, gates anything destructive behind exp

An MCP server that proxies tool calls to a running SentinelX Core instance with OIDC/OAuth authentication. It enables secure integration of SentinelX Core's HTTP agent capabilities through the MCP protocol.

Deterministic execution engine for AI agents. 412 modules across 78 categories including browser automation, file I/O, Docker, data parsing, crypto, and scheduling. Supports STDIO and Streamable HTTP transport with execution trace, evidence snapshots, and replay from any step.

An MCP server for coordinating trusted local AI agents across machines on a private Tailscale network, enabling message passing and state management between agents like Claude Code, Codex, and Ollama.

A dedicated server for Oracle Cloud Infrastructure (OCI) Core Services that enables management of compute instances and network operations with LLM-friendly structured responses.

A production-ready MCP gateway and control plane that provides credential vault, policy engine, audit logging, and managed runtime for routing tool calls between AI agents and downstream MCP servers.

Local one-shot YAML/JSON agent workflow CLI with tools, approvals, and resume, plus an optional stdio MCP server. Apache-2.0, BYOK.

Implicit is the experience layer for AI agents. Bounded local stdio MCP for synthetic experience addressing, selective state materialization, provenance, benchmarking, and adapter validation.

Run local AI across all your computers. Toskar's MCP server lets Claude, Cursor, and other clients ask your own local models and search your connected knowledge, with nothing leaving your machines.

Local MCP server for specialist personas, reusable Markdown workflows, task-scoped subagent contracts and per-repository memory. Runs on your own computer with Python 3.11+ and a locally installed embedding model; its Claude Code plugin launches that separate installation.

Connects any MCP-compatible AI client to Letta.ai's stateful agents for agent conversations, memory management, and tool orchestration.

Enables an agent to reconfigure its own harness fleet instead of relying on a human: activating skills just in time, switching MCP servers on and off, and reading or appending master instructions. It also reports fleet status so you can see which harnesses are currently linked.

Enables teams and their existing AI agents to join shared live sessions over MCP, where participants can watch an agent work, redirect it, and hand off tasks while each agent keeps its own credentials and machine.

Compiles YAML adapters or OpenAPI specs into a minimal MCP tool surface that exposes only declared capabilities to agents, with risk-tiered execution, human approval queues, credential brokering, prompt-injection sanitising and NIST AU-3 audit logging. This lets operators put scoped identity, permissions and kill switches in front of any API so agents can call exactly the tools they need and nothing more.

Exposes ReadyAgents' local workflow engine and built-in tools over MCP stdio, enabling MCP clients to define, run, inspect, and approve YAML/JSON agent workflows.

Enables MCP-compatible AI agents to discover and invoke provider skills with automatic multi-account API key rotation, encrypted key storage, and usage monitoring.

Enables AI clients to interact with Letta.ai's stateful agents via cloud deployment on Railway. Provides 20+ tools for agent management, conversations, memory operations, and tool configuration through a streamable HTTP transport optimized for production use.

webnav is an MCP server that gives web-browsing AI agents a reusable, deterministic site map, so they stop re-exploring sites every run. It enables page-to-page navigation with zero LLM overhead, pausing only at genuine decision points.

Enables AI agents to discover and execute named capabilities through an MCP SSE server, routing requests to backend providers such as HTTP, n8n, or mock implementations.