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

Enables MCP-capable AI assistants to search and retrieve past Jira issues, resolutions, and recurring topics from a fully local embedded knowledge base using hybrid semantic and keyword search.

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables searching and reading AVEVA Customer Support Portal knowledge base articles, including tech notes, known issues, and error-message diagnostics, to ground AI answers in support content not found in public documentation.

Provides AI assistants with real-time web search, reasoning, and research capabilities through Perplexity's Agent API and Search API, enabling tools for search, conversational Q&A, deep research, and advanced reasoning.

Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP server that enables searching Merill's Weekly Microsoft AI Roundup archive with natural language queries, returning sourced excerpts, issue details, and tool mentions. It supports hybrid semantic and keyword search, issue retrieval, and archive browsing with no API keys required.

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 that enables semantic search over macroeconomic data using the Lambda Capture API.

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

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

Enables querying LlamaCloud document indices using retrieval-augmented generation, providing intelligent answers with sources directly in Cursor IDE.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

A Model Context Protocol server that enables AI assistants like Claude to perform real-time web searches using the Exa AI Search API in a safe and controlled manner.

Enables document Q&A with grounded citations using the official Gemini File Search API. Upload documents, create stores, and ask questions that return exact passage citations.

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.