"Eight" matching MCP servers:

Enables developers and AI agents to check, price, pay for, and follow paid work on a network of AI agents over stdio or streamable HTTP, exposing eight tools where only ordering can spend and only behind four safety gates. Without a wallet key every tool stays free and ordering returns a dry run, so an agent can inspect plans and prices before any payment is signed.

Enables MCP clients to discover and create agents, durably submit messages with verifiable replies, and search source-linked local memory. Exposes eight authenticated tools with owner-approved OAuth, transcript-backed verification, and explicit control over history import and embedding.

A token-efficient browser automation server that enables LLMs to navigate websites, click elements, and extract content using accessibility trees and markdown conversion. It provides eight tools for lightweight browser interaction optimized for performance with local LLM endpoints.

An MCP control plane for Codex native sessions, translating eight explicit operations with strict security boundaries for authenticated access through OpenAI Secure MCP Tunnel.

Enables cross-harness host inspection, profile persistence, and execution configuration, with safe preview, apply, and validation of configuration changes.

Steers a coding model through a 0→10 step reasoning protocol by injecting a directive into the system prompt before inference, and hooks refusals to retry the request with a reframe and rotate to an open-weight model. Exposes tools, prompts, and resources for traffic-light state, history, health, and configuration over stdio, Streamable HTTP, or a Cloudflare Worker.

Gives a remote MCP client one read-only tool for asking a locally running Hermes Agent short questions, answered with that machine's own memory and session context, over bearer-authenticated Streamable HTTP. It exposes no terminal, file writes, messaging, or other action capability, and every other request is refused upstream of the model.

An MCP server that autonomously optimizes ONNX ML models for Arm64 deployment, providing tools to analyze models, apply real INT8 quantization, benchmark performance, and generate Arm64-optimized Docker deployment packages.