"Google Fit" matching MCP servers:

Enables voice assistants to research and rank family outings by checking live web sources, hourly forecasts, park alerts, and drive times against each household member's needs. It remembers family profiles and past visits so every recommendation comes back with a per-member fit verdict.

Enables searching Google Scholar with Scholar operators, year ranges, cited-by and all-versions lookups, and retrieving citations in five styles with BibTeX and EndNote export links as structured JSON.

Long-term memory and context fitting for agents, with no LLM in the loop: remember and forget messages, recall what is relevant within a token budget, and fit long text into a budget. Local SQLite by default.

Enables Codex, ChatGPT, and Aside to share project work memory by reading private GitHub-backed checkpoints and searching or re-reading allowed local/SSD documents through read-only MCP tools.

Enables MCP-speaking agents to search, read and write cards in a folder-based knowledge store and to capture decisions as proposals awaiting the owner's confirmation, with a small CLI for systems that can only run commands.

Privacy-first personal knowledge database for your AI that ingests data exports (Google Takeout first) and exposes them via MCP tools like search and list items. Runs locally with no cloud or telemetry.

Secure MCP server for querying Google NotebookLM notebooks. Enables AI assistants to list notebooks, read sources, and ask the NotebookLM AI questions about your sources.

MCP server that provides programmatic access to the SOLVE-IT digital forensics knowledge base, enabling LLMs to query, navigate, and search forensic techniques, weaknesses, mitigations, objectives, and citations.

Enables comprehensive management of Google Ads accounts by allowing users to create campaigns, manage keywords, and generate performance reports through a context-aware interface. It features a three-layer architecture that integrates product knowledge and historical campaign data to support intelligent decision-making.

Hosts three live read-only MCP servers enabling agents to retrieve and cross-reference curated fictional and real archives with provenance and source URLs.

An MCP stdio bridge that lets compatible agents query a local, provenance-first memory graph, fetch source-versioned text, inspect retrieval, check service health, and report usefulness feedback.

Official MCP server for creating, reading, and updating mind maps on coret.id from AI agents. Changes appear live in the Coret editor.

This advanced memory server facilitates neural memory-based sequence learning and prediction, enhancing code generation and understanding through state maintenance and manifold optimization as inspired by Google Research's framework.

Stores photos and notes as experience memories in Google Drive and enables natural language search to retrieve them later.

Enables AI assistants to record decision summaries when suggestions are rejected or corrected, and to search past decisions in later sessions so users do not repeat the same corrections.

Turn any website into a book you agent can read

Enables AI agents to run automated daily code reviews, retrieve Markdown reports, and curate a project knowledge base across any Git repository.

Enables AI agents to query Google NotebookLM directly for accurate, source-based answers, eliminating hallucinations by relying on user-uploaded documents.

Provides MCP server for interacting with Google Gemini Notebook, allowing users to authenticate via Chrome session and retrieve notebooks with full UTF-8 support.

Enables an AI assistant to keep every conversation verbatim in a local SQLite store and recall the most relevant past turns on demand, using hybrid keyword and embedding search plus time-awareness. Results are packed into a small token budget (2k–8k tokens) so long histories fit modest context windows, with secrets masked before storage.

Reduces token waste by caching code understanding in .mi context files and prevents agent conflicts by coordinating intent and file access across sessions.

Implements an MCP server that interacts with Google Sheets to store and retrieve prompts and ideas, allowing management of textual content with metadata across spreadsheet tabs.

A persistent, PostgreSQL-backed repository management system and semantic memory MCP server that stores the entire Git DAG natively in PostgreSQL with temporally-decayed semantic vector embeddings for AI IDEs.

An AI-native career copilot that enables job discovery, fit scoring, and application tailoring through natural language, running locally with zero API keys.

Enables AI assistants to query a curated esoteric and metaphysical corpus for correspondences, full entries, cross-references, searches, listings, and timing or fit recommendations. It exposes this reference data through MCP tools so assistants can answer relationship and provenance-aware questions in one step.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

Repository-native, Git-reviewable memory extension for AI coding assistants to persist project context across features, using SQLite caching for up to 10x token reduction.

Filters an agent's already-retrieved memories to fit a token budget using the memory_select tool, while keeping API keys in the environment. It works locally via MCP stdio or HTTP with optional JEV gateway support.

An MCP server that captures the rationale behind code changes at the moment they are written, binding decisions to commits with full provenance, and serves that context later without guessing. It records 'why code exists' and tracks anchor health over time.

Central MCP server for storing and querying lessons learned and project-specific implementation details, compatible with MCP-capable AI clients like Cursor, Claude Code, and Google Antigravity.