"Accessing A-Share Market Data" matching MCP servers:

Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.

Enables Codex and Claude to share Git-backed Markdown memories, assistant handoffs, and reusable skills through read-only and write-enabled MCP endpoints, while owners review revisions, conflicts, and deletion requests in a private GUI.

A local catalogue of the AI tools people share in group chats: search it, save links, vote out junk, and file items into your own folders. Works with any model or none.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Persistent memory for AI agents. Store, recall, and share knowledge across sessions with five MCP tools: remember, recall, context, forget, and share. Includes semantic search and agent/user/org scoping.

Read-only MCP access to the p-e relay store: an append-only corpus of provenance-bearing coordination records, with tools to fetch a record by id, list records and replies, and check existence. This image serves a snapshot frozen at build time; append_relay is listed but refused, and deposits to the live store require an HMAC-signed request.

Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.

Enables AI agents to search, share, and vote on community knowledge solutions via the Reposit platform.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

Connects AI assistants to Tacit building digital twins to query buildings, equipment, sensors, and zones using natural language. It provides tools for exploring Brick-compliant knowledge graphs, fetching historical sensor data, and accessing site documents.

AI agent knowledge marketplace where agents share solutions and earn tokens. Search, publish, and unlock previously solved problems to reduce token usage and computational costs.

Provides persistent, self-optimizing memory for AI agents, enabling them to remember preferences and context across sessions and share knowledge across multiple agents.

Non-custodial memory MCP server with client-side encryption, enabling secure remember, recall, forget, and share operations with post-quantum security.

A public, source-linked notebook for AI agents and people. Search and read without a key; publish findings, questions, and corrections with permanent IDs and linked replies using an operator-controlled posting key.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

A persistent world your agents share across sessions and models. MCP memory server with semantic memory retrieval, cross-agent handoff, a hosted remote endpoint with OAuth, a free 24-hour room, and an open-source local stdio server.

Enables coding agents to share a persistent local memory store across sessions and clients, saving, searching, updating, and retrieving facts, decisions, incidents, preferences, references, and snippets with semantic and lexical search.

Enables persistent, project-scoped memory for OpenAI Codex Desktop and CLI on Windows, capturing prompts and responses through managed hooks, with federated recall across projects and optional local graph extraction.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Enables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.

Provides a hybrid memory architecture with a thin SQLite index and Markdown cold storage, enabling AI agents to write, query, link, and rebuild long-term memories via MCP tools, model-agnostic and zero third-party dependencies.

A local-first MCP server that snapshots project working state into a structured context object, enabling agents to share and resume sessions seamlessly without leaving your machine.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

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

Enables several Claude windows or accounts to save and search a common set of notes, preferences, and decisions, and to leave topic-filed messages for each other that the recipient reads on demand. All data stays in two local JSONL files with no network access, so multiple instances can share context and hand off work without repeating background.

Enables searching and accessing Readwise highlights and documents through HTTP endpoints using the Model Context Protocol. Provides vector and full-text search capabilities with streaming responses for retrieving reading highlights and notes.

Enables AI agents to search and submit solutions, ask and answer questions, share findings, and vote on content via DebugBase's collective knowledge base.