Enables Claude and other MCP clients to list and query allowlisted Supabase tables, insert rows, and search, read, or create Notion pages. It ships with table allowlists, validated inputs, row limits, and an optional read-only mode that unregisters write tools, plus a mock mode for trying it without credentials.

Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift

A local Python + SQLite MCP memory service that stores persona, shared experiences, facts, agreements, and unfinished work, then restores them within a context budget so a new model can continue the same conversation. It supports offline original-text persistence, Chinese keyword retrieval, provenance-tracked memories, persona versioning/rollback, and progressive fading of older events.

Enables MCP clients to curate YouTube watch history and locally search selected transcripts using SQLite FTS5, ChromaDB embeddings, and reciprocal rank fusion, with tools to browse unindexed history and add videos.

Enables local creation and querying of projects, storage and retrieval of source text, reading and saving storyboard tables with revision-conflict protection, and Excel export through MCP tools backed by SQLite.

Exposes a governed dbt MetricFlow semantic layer to AI agents through three tools — list metrics, list dimensions and query metrics — so questions about federal contract spend are answered from defined metrics rather than raw tables. Guardrails reject undefined metrics, raw columns, SQL in filters and bulk exports, and no tool can execute SQL.

Enables AI agents to store decisions as a versioned history, linking each new decision to the one it replaces so that recall always resolves to the decision currently in force instead of obsolete ones. It runs fully locally on a single SQLite file with BM25 search, and can optionally detect replacements automatically via an LLM or heuristic linker.

Local MCP server that gives coding agents a verifiable project memory, trims what enters their context, and masks secrets — with honest accounting of what it saves and costs.

Local MCP server that gives coding agents a verifiable project memory, trims what enters their context, and masks secrets — with honest accounting of what it saves and costs.

EPO/USPTO search, citations, OCR, plus PATSTAT Portfolio Analytics, guarded SQL and Graph Analytics.

SettleMesh CLI + MCP server: deploy an agent-built app to a live URL with login, a managed database, usage-based billing, and end-user-pays — plus metered web search, LLMs, image/video, and a service catalog, all on one key.

Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.

Enables host assistants to discover and read the toolkit’s research methods over a local stdio MCP connection, supporting research planning, literature work, scientific reasoning, writing, figures, and academic document workflows. It does not itself search scholarly databases, generate research results, compile documents, or call models.

Exposes four read-only tools over extracted Hacker News "Who is hiring?" job posts — postings_search, posting_get, skills_top, and skills_trend — so an MCP client or agent can search postings, look up individual posts, rank top skills, or chart skill trends over time. Tools return only structured extracted fields and HN links (never raw post text) from a read-only SQLite database, including salary sorting and a hard row limit to keep queries cheap.