"Understanding HTTP Requests" matching MCP servers:

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

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.

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

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

MCP Compass is a discovery and recommendation service that assists AI assistants in finding and understanding Model Context Protocol servers through natural language queries.

Provides seamless access to Grok AI's capabilities (chat completion, image understanding, and function calling) directly from Cline via the Model Context Protocol.

Enables AI assistants to access contextual knowledge from multiple repositories through Sequa's Contextual Knowledge Engine. Provides architecture-aware code understanding and cross-repository context for more accurate, production-ready code generation.

A Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

An MCP server that gives AI agents structured code understanding and precise code intelligence via local indexing of AST, call graphs, and semantic search.

Stores metadata for MCP servers and provides smart search capabilities, allowing users to find appropriate MCP servers for their queries and route requests to the most suitable server.

A reference implementation for creating an MCP server supporting Streamable HTTP & SSE Transports with OAuth authorization, allowing developers to build OAuth-authorized MCP servers with minimal configuration.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

A lightweight, zero-configuration MCP server for source code analysis with GraphRAG capabilities, enabling structural understanding and efficient code completion from MCP-compatible AI tools.

{ "mcpServers": { "loomem": { "command": "loomem-cli", "args": ["mcp-stdio", "--url", "http://127.0.0.1:3030"] } } }

A lightweight, powerful local memory server for AI agents supporting text, entities, and relations. Enables persistent codebase understanding and user preference management.

This advanced memory server facilitates neural memory-based sequence learning and prediction, enhancing code generation and understanding through state maintenance and manifold optimization as inspired by Google Research's framework.

Enables AI assistants to query and analyze databases using natural language through MCP and HTTP API, supporting 17+ databases and integration with 50+ platforms.

An HTTP-based Model Context Protocol server for RiotPlan that exposes comprehensive planning tools, resources, and prompts for AI agents. It enables full plan lifecycle management, including ideation, shaping, execution tracking, and retrospective generation.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.