"A server for stock market data and analysis using ChainGPT MCP protocol" matching MCP servers:

A Model Context Protocol (MCP) server for interacting with MongoDB stock data to provide portfolio recommendations and market insights, exclusively for the Indian stock market (NSE/BSE).

A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

A read-only MCP server for exploring and analyzing a Secoda data catalog, enabling AI chat, semantic search, glossary browsing, and data lineage tracing.

Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.

A lightweight MCP server for intelligent, non-invasive analysis of Obsidian vaults.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Resume-as-an-Agent (RAAA) or Portfolio-as-an-Agent (PAAA) allows Agents to talk to your resume, find out if you are available for work, and more. Use the MCP to allow recruiters, managers, and anyone with an agent to "talk" to your resume.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

MCP server for persistent project knowledge management, enabling AI assistants to store, search, and reuse project analysis insights with freshness checking and impact analysis.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

An enhanced memory management system that wraps memento-mcp with sophisticated features including protocol enforcement, quality scoring, hybrid search strategies, and synthesis reports. Enables intelligent memory storage, retrieval, and analysis with automatic archival and confidence tracking.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

MCP server that parses aviation accident reports (PDF/DOCX/TXT) and injects their content into extraction prompt templates for knowledge graph construction.

Look up countries, timezones, periodic table elements, physical constants, units, HTTP status codes, and MIME types via MCP. STDIO or Streamable HTTP.

Enables AI coding agents to save task context as a validated JSON document and later restore it as a concise briefing with the objective, status, relevant files and focus lines, decisions, and prioritized next steps.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.