"Using Jira MCP API in Python" matching MCP servers:

AI reads your codebase like a book - one page at a time. Pharaoh gives it the index and a map of how every chapter connects.

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.

Provides a local-first, provenance-aware memory layer that enables MCP-capable AIs to store, recall, validate, and reason over facts with contradiction detection, trust weighting, deduplication, and encryption, supporting offline private operation without GPUs or API keys.

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.

MCP server for managing project memory in AGENTS.md. Allows agents to record and remove durable facts using save/forget tools.

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.

MCP server for persistent memory, enabling AI agents to store and semantically retrieve notes, decisions, and context across conversations using API keys or x402 payments.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

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

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

Provides shared real-time context for Claude Code agents, including scope awareness, prior decisions, and anti-overlap, using only files in the repo.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

Enables AI assistants to search, create, and organize content in your Capacities.io knowledge base, with limitations on reading object content due to API restrictions.

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.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

A general-purpose MCP server providing web search, persistent memory storage, and secure code execution capabilities. It enables AI agents to search the web, store and retrieve data, and run Python/JavaScript code in sandboxed environments.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Self-hostable, markdown-native team wiki with a built-in MCP server: agents search, read, and write your wiki pages (ranked Postgres full-text + semantic search, backlink traversal). Plus Atlas, which auto-generates a cited, coverage-checked wiki from your git repos and Jira.

An MCP server implementation that enables interaction with the Letta API for managing agents, memory blocks, and tools in the Letta system.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.