"RTM" matching MCP servers:

Enables autonomous agents to discover and bid on unused LLM token quota through a machine-to-machine secondary exchange protocol. It utilizes a Proof-of-Residue handshake to verify compute capacity and facilitate the real-time acquisition of verified residue tokens.

MCP server for Google Tag Manager and Google Ads, enabling AI agents to audit, fix, and manage GTM containers and Google Ads campaigns programmatically.

Enables AI agents to perform go-to-market workflows such as company and contact enrichment, CRM querying, and controlled, audited writes to a CRM through MCP tools callable from any MCP client.

Portable memory protocol for AI coding sessions. Push the state of a session (goal, decisions, attempts, next step) from one MCP-capable agent and pull it back from another.

Enables self-driving LinkedIn prospecting from AI assistants, including finding leads, warming them up, inviting them, and opening conversations, with human approval required before any message is sent.

A Telegram-integrated MCP server that enables remote control of Claude sessions through a Telegram bot, including chat, tool approvals, task tracking, and workspace management. It provides a MiniApp interface for real-time monitoring and control of Claude's activities from mobile devices.

Enables AI agents to build and operate HubSpot revenue operations work — workflows, lists, campaigns, sequence enrollments, owner reassignments, and record deletion — with read, build (switched off), and commit actions gated by preview-then-approve. Every write is read back from the live system and reported as verified only when the change is actually present.

An implementation of the Recursive Language Models architecture that enables AI agents to process massive documents by programmatically decomposing them into sub-queries. It allows for cost-effective and accurate reasoning across millions of tokens by treating long-form data as an external environment for root and worker models.

MCP server that enables verified code execution with LLM reasoning using Recursive Language Models (RLM). It supports tasks like code generation, data analysis, and complex task decomposition.

Provides persistent memory and semantic file discovery for AI coding agents, enabling them to remember changes and find relevant files across sessions.

Enables orchestrating multi-agent AI coding workflows with Git worktree isolation, parallel testing, and security scanning via the Model Context Protocol.