"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP servers:

A production-ready MCP server template that connects LLMs and AI agents to external data, tools, and services with built-in OAuth 2.1 authentication, Redis-backed session management, and a modular tools engine.

A basic Python implementation of a Model Context Protocol server for educational purposes, using FastAPI and WebSockets.

MCP server for exploring and querying Supabase databases with interactive React widgets for schema browsing, table viewing, and SQL execution.

MCP server that inspects local codebases and generates source-grounded context packs for coding agents, with tools for repository search, symbols, dependencies, Git analysis, and context generation.

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Local-first documentation for AI agents

Deterministic dependency + CVE context for AI coding tools, over the Model Context Protocol. A ~0.85 MB pure-Rust MCP server.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

Durable project-memory MCP: decisions, constraints, and pipelines across Claude sessions

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

Enables AI coding agents to consult a project's Git-versioned Markdown knowledge base over local stdio — checking knowledge health, listing the catalog, searching topics, and reading guides before acting. Access stays read-only, so project guidance, decisions, and lessons learned remain shared with human teammates and traceable beside the code.

Enables creating Spotify playlists based on text descriptions by connecting Cursor editor to Spotify's API through OAuth authentication.

Provides local, explicitly scoped memory for coding agents via MCP, storing durable project knowledge in a per-repository SQLite database with tools to record, search, and retrieve context.

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables LLM agents to compress handoffs into structured, auditable context capsules, preserving goals, constraints, decisions, and risks without external API calls.