"Information about Gmail MCP Node" matching MCP servers:

Composes arbitrary MCP tools using JavaScript, enabling discovery, composition, and execution of other MCP servers through a single 'exec' tool.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

Review & approve AI agent task plans in a ComfyUI-style node editor, right in your terminal (MCP server + TUI)

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Discover verified AI inference nodes across operators and imported catalogs, get the best node and model for a task, and pre-price multi-step chains with enforceable estimates and signed receipts.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Enables cluster-aware command execution and automatic task routing across distributed nodes based on system load, architecture, and OS requirements. It supports parallel execution, remote node management via SSH, and dynamic load balancing for agentic workflows.

A lightweight Model Context Protocol (MCP) orchestrator designed for efficiency at scale. It features TOON compression (reducing token usage by 30-90%) and Lazy Loading, making it the ideal solution for complex, multi-tool agentic workflows.

An MCP server that demonstrates sampling, enabling the server to request LLM completions from the client to assist in workflow tasks like planning work sessions.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

Provides an agent-first note-taking system designed from the ground up for AI collaboration. Organizes your notes as a local vault of ordinary markdown files with semantic note types.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Enables AI agents to coordinate atomic distributed transactions across a swarm through a prepare/vote phase, unanimous commit-or-abort consensus, quorum validation, and failure recovery. It also layers in vector-clock causal stamping, LWW CRDT reconciliation, and gossip dissemination so multi-node state converges deterministically without external dependencies.

Provides a hosted remote MCP server that lets AI agents create and use their own email address to send, receive, verify, and reply to messages.

A server implementation of the Model Context Protocol that enables calling iFlytek workflows, supporting complex orchestration modes like sequential, parallel, and looped execution across 14 node types for diverse business scenarios.

Enables AI clients to access many MCP servers through localhost HTTP endpoints, running each server once in a single Node process to avoid duplicated stdio spawns.

Enables natural-language onboarding, deployment, and management of edge nodes and their per-node MCP servers, including skill installation, app deployment, and fleet-wide tool calls over SSH.

An MCP server that aggregates multiple upstream MCP tools into a single 'codemode' tool for unified orchestration and execution. It enables agents to run generated code in an isolated sandbox to interact with various connected services through a single interface.

A unified mesh control layer that provides 30 MCP tools for managing distributed infrastructure, enabling AI agents to execute commands, transfer files, and manage Docker containers across multiple nodes. It features persistent SSH2 connection pooling and cross-node configuration synchronization to unify disparate servers into a single interface.

A transparent message ferry between Claude Desktop and Claude Code CLI, enabling task delegation with any MCP configuration without the bridge needing to know about specific MCPs.

A modular backend that connects language models to real-world tools like Gmail and Google Docs via MCP server abstractions, enabling agentic workflows with LLM reasoning and secure OAuth integration.

The Execution Security Layer for the Agentic Era. Providing deterministic "Sudo" governance and audit logs for autonomous AI agents.

Bridges Claude Code terminal sessions with Claude Chat by exposing session logs as tools, enabling seamless context transfer and two-way communication via a cooperative inbox and optional Google Drive sync.

Enables developers to host an MCP server over Streamable HTTP on Node with corrected response framing, client elicitation capability reporting, and lightweight SSE parsing for progress and mid-stream elicitation events.

Enables AI agents to control isolated Chromium browser identities, allowing per-account navigation, interaction, and inspection through MCP without requiring Node or CLI installation.

Provides a single local MCP endpoint that connects Gmail, GitHub, Slack, Notion, and Linear once, with multiple accounts per service, so any AI agent can use per-agent tokens, policies, and optional result filtering without re-authenticating.

Enables Codex to orchestrate multi-agent coding by routing tasks to local Claude Code workers via CCSwitch, managing cost and model selection for planning, execution, and review.

Enables clients to discover capabilities and compose tool calls with structured responses through a remote MCP endpoint running on Cloudflare Workers.

Enables secure enterprise AI agents to access internal tools like GitHub, Gmail, Calendar, file systems, databases, and knowledge bases through the Model Context Protocol, with built-in security, audit, and observability.