"Ways to make money" matching MCP servers:

A money-in-politics knowledge graph, as an MCP server. It links entities across federal campaign donations, lobbying disclosures, and government contracts.

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Make it easy for agents to build their context about your projects over time The server provides a set of tools to help agents accumulate knowledge about a project over time in a structured way.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Enables AI assistants to make phone calls via CallZero service, with tools for call management, templates, and memory.

Provides a local-first memory layer for coding agents, storing principles, rules, and corrections on-device and surfacing them at the moments they apply so agents act on the intended outcomes without sending data to the cloud.

Enables AI agents to store and retrieve memories with user-specific context using Mem0, allowing them to maintain conversation history and make informed decisions based on past interactions.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

Enables agents to access a shared portable vault for memory, skills, and API credentials through MCP, so tools can store and retrieve memories, read versioned skill playbooks, and make server-side API calls without exposing secret keys.

Combines bm25, vector and knowledge graphs to create a comprehensive memory solution. https://github.com/lumetra-io/engram-memory-mcp

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Enables coding agents to query a local, versioned knowledge graph of a software project, retrieving overviews, context packs, evidence, and explanations to make informed changes.

Enables Claude to act as a personal assistant in Telegram by providing tools to read new messages, send replies, and manage tasks/reminders/notes in a Supabase-backed memory.

MCP server that connects Claude to a Telegram personal assistant, enabling message processing, task management, and responses via scheduled workflows.

Caches Telegram channel posts locally in SQLite and exposes them via MCP tools for querying and management.

An MCP server implementing the Will-Aware Knowledge Execution (WAKE) protocol for post-mortem AI agent management, enabling heartbeat monitoring, death verification, and execution of succession plans.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.