"Google Earth Engine" matching MCP servers:

MCP server that exposes Google Jules AI agent as tools, allowing local AI agents to list authorized repositories and delegate coding tasks to Jules, receiving session URLs for tracking.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

Enables AI tools to perform real-time Google Search with grounded answers and inline citations, free with a Google account.

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

Enables finding files and knowing their location and permissions, organizing folders, moving and copying content, transferring files in and out, managing sharing with transparency, tracking changes, and handling shared drives.

Enables reading, searching, and writing to Google Chat spaces, direct messages, and sidebar through MCP clients such as Claude Code and Claude Desktop.

Provides access to Google Calendar, enabling listing calendars and events, checking availability, creating and updating events, and managing sharing through MCP.

Enables natural-language interaction with Gmail, including reading, searching, drafting, and managing emails, with safety features like read-only mode and bounded reads.

Enables reading and writing spreadsheet ranges without destroying formulas, plus reshaping, formatting, and validating data within a spreadsheet.

Enables reading, editing, suggesting, and commenting on Google Docs from any MCP client, with scoped reads, minimal-diff edits, and support for tables, tabs, and layout.

Local CI/CD engine for AI agents. ActionD listens to LGH git events and runs plugin jobs (Go/Python/Java/web toolchains) in isolated checkouts, with structured results, root-cause failure diagnosis, approval gates, and auto-rollback. The MCP server exposes 23 tools, including dev_cycle_run (commit → CI → structured verdict in one call) and actiond_diagnose for failed-job analysis.

MCP server for AI-powered web research — search (Google, Brave, Serper, SearXNG), scrape any page, extract PDFs/DOCX/YouTube transcripts, academic & patent search. Single Go binary. Works with Claude, Cursor, Copilot, and any MCP client.

Read-only attack-path tools for cloud and Kubernetes: list the reachable routes from internet exposure to sensitive assets, explain each hop and the evidence behind it, and simulate a fix before recommending it. Connects to a running PerspectiveGraph engine.

Multi-account Google Workspace MCP server — Gmail, Calendar, and Drive with smart routing.

Lets AI agents drive terminal apps built with Limoni (a Go TUI engine) through their semantic tree instead of scraping the screen: widgets are found by role and label, clicked, typed into, and waited on. Runs locally over the app's automation socket; secret fields never reach the agent.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

MCP server for patent search and prior art discovery powered by Google Patents public dataset on BigQuery. Supports searching patents, fetching full patent details with CPC codes and citations, and retrieving legal claims text.

Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.

MCP server for managing Apple Wallet and Google Wallet passes via the PassNinja API. Provides tools to create, list, update, and delete pass templates, passes, and webhooks.

Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON