FastMCP is a production-oriented Python framework for the Model Context Protocol (MCP). Rather than being a single application, it is a multi-package library stack: `fastmcp_slim` provides the core server/client framework, `fastmcp_remote` bridges remote MCP servers to local stdio, and `fastmcp_tasks` implements SEP-2663 background task execution. The core architecture centers on the `FastMCP` server class, a `Client` class with transport abstraction (stdio, SSE, Streamable HTTP, in-memory), provider-based component registration (local, filesystem, OpenAPI, proxy, skills), composable middleware, transforms, OAuth/OIDC authentication, OpenTelemetry instrumentation, and a CLI for running, installing, deploying, and inspecting servers. Entry points include the `fastmcp` CLI and the Python APIs `FastMCP`, `Client`, `ClientGroup`, `FastMCPApp`, and `TasksExtension`. This is an application framework, not a data/research repository, though it contains information-retrieval components (BM25 and regex tool search), OpenAPI schema conversion machinery, and telemetry utilities that approach data-engineering utility. The codebase demonstrates strong engineering discipline: extensive unit and integration tests, conformance testing against the MCP spec, lazy imports to preserve startup performance, upgrade-guide tests that verify documentation claims, and a transform/middleware system that keeps extensions decoupled from core server logic. External dependencies include the MCP Python SDK, Pydantic, Starlette/ASGI, OpenTelemetry, optional authlib, optional prefab-ui for interactive apps, and an internal `httpx2` fork, which is the most notable maintenance risk in an otherwise current codebase.
This repository contains a sophisticated COBOL modernization toolkit that uses AI agents to analyze, extract business logic from, and convert legacy COBOL code to modern Java (Quarkus) or C# (.NET) applications. The system features an agent-based architecture with specialized agents for structural analysis, business logic extraction, dependency mapping, and code conversion. It includes a web portal for monitoring migration progress, visualizing dependency graphs, and interacting with AI assistants. The toolkit handles large COBOL files through intelligent semantic chunking that preserves code context across chunks, and uses a hybrid persistence approach (SQLite for metadata, Neo4j for graph visualization) to store migration artifacts. Key innovations include a three-tier complexity reasoning system that dynamically adjusts AI effort based on code complexity, semantic unit-aware chunking, and cross-chunk consistency mechanisms for type mappings and signatures.
This repository is the official home of the Model Context Protocol (MCP), an open standard defining how AI assistants discover and invoke external tools, resources, and prompts over JSON-RPC. In production it operates as a specification and governance hub: versioned TypeScript protocol schemas (2024-11-05 through 2026-07-28 plus draft) are compiled into JSON Schema artifacts, human-readable specification docs are generated and published, and a SEP (Specification Enhancement Proposal) process is automated by a GitHub bot that detects proposals, enforces lifecycle transitions, pings authors, and sends Discord notifications. The architecture is not a monolithic service or microservices; it is a mixed content-plus-tooling repository. The primary code components are the TypeScript schema sources, the schema/doc generation scripts (scripts/generate-schemas.ts, scripts/render-seps.ts), and the sep-automation Node.js application (tools/sep-automation), which uses explicit interfaces for the GitHub client, hooks, and workflow rules. Core data flows are: TypeScript interfaces -> JSON Schema -> published schema files and examples; SEP markdown -> rendered MDX documentation; and GitHub issue/PR events -> SEP state machine -> comments and Discord webhooks. External dependencies include Octokit/GitHub App auth, Discord webhooks, TypeDoc/typescript-json-schema, Mintlify for docs, and Hugo for the blog. Although classified as an application repo because of its runnable tooling, CI/CD, and unit tests, the dominant character is an open-standards documentation and governance project. It is actively maintained, enforces a conformance-testing mandate for SDKs, and contains well-crafted modular code. The main risks are content-version overhead (six dated spec snapshots plus draft, duplicated docs, frozen historical SEPs) and process/doc drift, not software decay.
Nanobot is a self-hosted, open-source personal AI assistant framework that operates as a full-stack application. The Python backend provides an agent loop with tool execution, session management, multi-provider LLM orchestration (OpenAI, Anthropic, etc.), and a plugin system for 18+ messaging channels (Telegram, Discord, Slack, WeChat, Feishu, etc.). It exposes a WebSocket-based gateway, an OpenAI-compatible API, and a CLI. Two frontends exist: a rich WebUI built with React/TypeScript/Vite/Tailwind and a terminal TUI built with Bun/OpenTUI. The architecture is event-driven via a message bus, with decoupled channel adapters, provider abstraction, and a config system supporting hot-reload. It includes security features like workspace sandboxing, credential redaction, and SSRF protection. The codebase is extensively tested (hundreds of unit/integration tests) and demonstrates strong engineering practices with clean abstractions, though its large dependency surface and multiple frontends introduce some maintenance surface area.
The drawio-mcp repository provides a comprehensive ecosystem for AI-assisted diagram creation using the Model Context Protocol (MCP). It consists of four integrated approaches: an MCP App Server for inline diagram rendering in AI chat interfaces (deployable as Node.js or Cloudflare Worker), an MCP Tool Server for opening diagrams in the draw.io browser editor, assistant-specific plugins for Claude Code, Codex, and GitHub Copilot, and project instructions for Python-based diagram generation. The system enables AI assistants to create diagrams from Mermaid or XML input, apply layouts (ELK/libavoid), and export to various formats while emphasizing data residency controls—self-hosted options keep all diagram data local, with cloud export requiring explicit user action via draw.io Desktop CLI.
Vigil is an open-source AI SOC (Security Operations Center) platform that orchestrates autonomous threat hunting, incident response, and case management through a multi-agent architecture. The system ingests security findings from SIEMs (Splunk, Elastic, Azure Sentinel), EDRs (CrowdStrike, Microsoft Defender), and other sources via Kafka or webhooks, then uses LLM-driven agents (hunt, root_cause, adjudicate, investigate, compose, chat) to analyze, enrich, and respond. A TypeScript agent layer with event-sourced ledgers and budget enforcement runs investigations, while a Python daemon handles polling, deduplication, and orchestration. The platform exposes its own tools via MCP (Model Context Protocol) and integrates with 30+ external security tools through a descriptor-based plugin system. The frontend is a React SPA with a setup wizard, dashboards, and case management UI. Deployment is via Docker Compose or Helm, with PostgreSQL, Redis, and optional Kafka as backing services.
The Dext Framework is a comprehensive, modular full-stack framework for Delphi (v11+) designed to bring modern web development patterns to the native Delphi ecosystem. It is Apache 2.0 licensed and provides a coherent set of libraries covering web API construction (minimal APIs, MVC controllers, middleware pipeline, SignalR-compatible real-time hubs), an ORM with code-first migrations, dependency injection, background jobs, event bus, resilience pipeline, testing/mocking utilities, desktop UI patterns (MVU/MVVM with VCL binding), AI integration (MCP server, agent graph), CLI tooling, and telemetry/dashboard. The architecture is heavily inspired by ASP.NET Core/EF Core, with fluent builders (`TDextServices`, `TApplicationBuilder`), specification pattern, attribute-based configuration, and a focus on zero-allocation performance (custom SIMD-optimized collections, ring-buffer logging, direct codecs). It includes extensive documentation (The Dext Book in English and Portuguese), benchmark harnesses, and 40+ example applications covering real-world use cases from simple CRUD to multi-tenant e-commerce. The codebase is actively maintained, with multi-version Delphi support (XE2 through 13), community database drivers (UniDAC), and a growing ecosystem of IDE integration and design-time tooling.
mcporter is a portable Model Context Protocol (MCP) runtime, CLI, and code-generation toolkit. It connects to configured MCP servers—over stdio, HTTP/SSE, or Chrome DevTools relay—and exposes their tools via a rich command-line interface (subcommands: list, call, resource, auth, config, serve, daemon, generate-cli, emit-ts, record, replay) and a programmatic API (createRuntime, createServerProxy). The architecture is modular: a config layer merges JSON/JSONC files and imports from common editor configs (Cursor, Claude, VS Code); a runtime layer manages transport connections with OAuth support, record/replay, and error classification; a daemon process pools keep-alive servers for low-latency reuse; and a codegen subsystem produces standalone CLIs or typed TypeScript clients from server schemas. The codebase is heavily tested (~150 test files, coverage ≥89.6%), follows modern TypeScript patterns, and supports protocol versions from 2024-10-07 to 2026-07-28. It is not a data pipeline or research project—it is a production-grade developer tool for integrating MCP-powered agents and workflows.
OpenRath is a framework for building LLM-powered AI agent applications with a torch-like API design. It centers on the Session abstraction (analogous to a Tensor) that flows through Sandboxes (execution environments), Memories (persistent state), and Tools (callable operations), orchestrated by Agents and Workflows. The framework provides session lineage tracking, sandboxed tool execution, memory persistence, and LLM provider abstraction, enabling the creation of multi-agent workflows with durable state and traceable provenance. The architecture is modular and extensible, featuring pluggable backends for sandboxes (local, OpenSandbox) and memory systems (local, OpenViking), a registry pattern for dynamic backend loading, and lazy evaluation of sessions for performance. Key innovations include write-ahead logging for crash-resistant session persistence, resource-keyed parallelism for safe concurrent tool execution, and unified garbage collection across session, sandbox, and memory domains. The framework is designed for production use, with extensive testing, clear documentation, and active maintenance.
Exa MCP Server is a Model Context Protocol server that connects AI agents to Exa's web search and research capabilities. It provides a standardized interface for agents to perform web searches, fetch webpage content, run multi-step research agents, and access other Exa functionalities. The architecture features clear separation of concerns with API handlers for Vercel deployment, core MCP server logic, tool implementations for each Exa capability, and utility functions for authentication, logging, and error handling. Key entry points include stdio CLI for local usage and Vercel HTTP functions for cloud deployment. The system handles authentication via API keys or OAuth, implements rate limiting for free tier users, and includes mechanisms to prevent API key leakage between requests.
The UTCP-MCP Bridge is a multi-language implementation that exposes Universal Tool Calling Protocol (UTCP) tools as Model Context Protocol (MCP) tools, enabling MCP clients like Claude Desktop to discover and call any UTCP-registered tool. The system consists of MCP servers (in Node.js/TypeScript, Python, Rust, and a web-based implementation) that initialize a UTCP client, register providers from configuration, and expose standard MCP tools for tool management (register/deregister manuals, call tools, search tools, list tools, get tool info, and get required variables). The architecture cleanly separates MCP server concerns from UTCP client operations, with each language implementation following similar patterns but idiomatic to its ecosystem. The bridge handles MCP specification nuances like proper content block preservation for binary data and provides dynamic provider management through REST APIs in the web UI.
This repository implements a deep research assistant web application that combines large language models (LLMs) with web search capabilities to generate comprehensive research reports. Built with Next.js 15, React, and Tailwind CSS, it features a client-side interface for topic input and research workflow management, backed by API routes that proxy requests to external AI and search services to protect API keys. The system orchestrates an iterative research process: planning sub-queries, executing parallel searches across multiple providers (Tavily, Firecrawl, Exa, etc.), analyzing results with auto-review cycles, and generating cited final reports. Key architectural elements include Zustand-based state management with localForage persistence, a Model Context Protocol (MCP) server for standardized tool/resource exposure, PWA support for offline installation, and modular components for knowledge base management, file parsing, and real-time progress streaming via Server-Sent Events (SSE).
WhoDB is a self-hosted, multi-database management platform with a GraphQL Go backend, React/TypeScript frontend, CLI (Cobra/Bubble Tea), desktop app (Wails), and multi-language SDKs (TypeScript, Python, Go, Rust, Java). It provides schema exploration, CRUD operations, raw query execution, import/export (CSV, Excel, NDJSON), mock data generation, schema diff, and AI-assisted SQL generation via BAML and LLM providers (OpenAI, Anthropic, Gemini, Ollama, LM Studio). The system supports PostgreSQL, MySQL, ClickHouse, SQLite, DuckDB, MongoDB, Redis, Elasticsearch, Memcached, and cloud-managed databases from AWS, Azure, and GCP. It also includes an MCP server for AI assistant integration, a hosted platform SDK, and enterprise/community edition separation via extensible registries.
This repository is the official Go SDK for the Model Context Protocol (MCP), a client/server protocol for AI tool orchestration. It provides production-grade implementations of MCP servers and clients, supporting multiple transports (stdio, SSE, streamable HTTP, in-memory) and full OAuth 2.0 authorization code flows with enterprise extensions. The SDK features typed tool handlers with automatic JSON Schema inference, a middleware system for custom processing, generic pagination helpers, and a schema cache for performance in stateless deployments. It maintains backward compatibility with multiple MCP protocol versions (2024-11-05 through 2026-07-28) using compatibility flags and includes conformance tests against the official MCP test suite. The architecture separates JSON-RPC 2.0 wire protocol, transport abstractions, and core client/server logic, with internal packages for low-level RPC handling, OAuth test infrastructure, and configuration compatibility. While the SDK is actively maintained and well-tested, it carries some technical debt from deprecated features (roots, sampling, logging) and protocol version complexity.
pREST is a production-grade Go HTTP server that automatically exposes PostgreSQL databases as RESTful and MCP (Model Context Protocol) APIs without requiring hand-written backend code. It provides instant CRUD endpoints for any table, custom SQL template execution with parameterized queries, and a built-in Studio web UI for exploring and managing APIs. The system is architected around a hexagonal pattern with a clean adapter interface supporting multiple databases (PostgreSQL, TimescaleDB), dependency injection for controllers, and a layered middleware stack for authentication (JWT), authorization (table/field-level ACL), caching, and CORS. Security is a primary concern: SQL injection is mitigated through strict identifier validation, template parameter screening, and bound parameter binding, while credential leakage in logs is prevented by a dedicated redaction package. The codebase is well-tested with both unit tests (using sqlmock) and integration tests (against real PostgreSQL in Docker), and includes OpenTelemetry integration for observability. It is not a data research or analytics project; it is a pure application for building low-code database APIs.
Vibe Check MCP is a production-ready MCP (Model Context Protocol) server that provides metacognitive oversight for AI agents. It implements the research-backed Chain-Pattern Interrupt (CPI) methodology to prevent reasoning lock-in by injecting reflective pauses during agent workflows. The server exposes six tools: `vibe_check` for generating clarifying questions via a secondary LLM, `vibe_learn` for logging patterns of mistakes and successes, and three constitution management tools (`update_constitution`, `reset_constitution`, `check_constitution`) for per-session behavioral rules. It supports multiple LLM providers (Gemini, OpenAI, Anthropic, OpenRouter) with automatic fallback to static questions when the LLM is unavailable, and maintains session history for context-aware feedback. The architecture is a monolithic MCP server with dual transport modes: stdio for local agent clients (Claude Desktop, Cursor, Windsurf, VS Code) and streamable HTTP for remote access. A CLI tool (`vibe-check-mcp`) automates installation into all major MCP client configurations with atomic writes and backup creation, handles secret management via interactive prompts or env files, and provides diagnostic commands. The server includes robust HTTP security hardening: DNS-rebinding protection via Host header validation, loopback-only CORS by default with a configurable allowlist, explicit JSON body size limits, and proper JSON-RPC error responses instead of HTML pages. The codebase is well-organized with separation of concerns, extensive unit and integration tests (over 30 test files), CI/CD with security scanning, and clean abstraction layers such as a central model registry and request-scoped transport wrappers.
This repository contains a VS Code extension that acts as a bridge between the Antigravity IDE and a mobile device, enabling remote interaction via a local HTTPS server. The extension starts a self-signed HTTPS server on the LAN, uses the Chrome DevTools Protocol (CDP) to inspect and manipulate Antigravity IDE instances, and leverages the Language Server Protocol (LSP) to monitor and control code generation processes. A companion mobile web interface, served by the local server, provides real-time chat mirroring, message sending, file uploads, voice input, and generation control through a WebSocket connection. The architecture consists of three main parts: the VS Code extension (managing the server lifecycle and IDE integrations), the Express/WebSocket server (handling API requests, real-time communication, and static file serving), and the mobile SPA (rendering the IDE state and forwarding user interactions). Key capabilities include QR-code based pairing, end-to-end encrypted local communication, multi-instance switching, and programmatic control via an MCP server that exposes the API over stdio JSON-RPC. The extension demonstrates solid engineering practices with TypeScript, modular service layers, middleware for authentication and security, and a test suite covering core functionality. While tightly coupled to Antigravity's UI and protocols by necessity, it isolates concerns through well-defined services (CDP, LSP, antigravity interactions) and reusable patterns such as the CSP middleware, CDP wrapper, and stdio JSON-RPC proxy. Ongoing maintenance addresses selector fragility and API updates, ensuring compatibility with evolving IDE and VS Code versions.
mcp-agent is a Python framework for building, deploying, and serving AI agents that interact with the Model Context Protocol (MCP). In production, it provides a library and CLI toolkit that allows developers to compose agents using configurable LLM providers (OpenAI, Anthropic, Azure, Bedrock, Ollama, Google), connect to any MCP server (stdio, SSE, Websocket, Streamable HTTP), and orchestrate multi-step workflows with patterns like parallel fan-out/fan-in, router, orchestrator-worker, evaluator-optimizer, and swarm. The framework is architecture-agnostic, supporting both asyncio in-process execution and durable execution via Temporal for long-running, fault-tolerant agent loops. The system ships with a feature-rich CLI (mcp-agent) for local scaffolding, configuration, and cloud deployment to MCP Agent Cloud, including secret management, log tailing, and OAuth integration. Key entry points include the main application bootstrap (MCPApp), the Agent class (managing MCP server connections and LLM), workflow implementations in src/mcp_agent/workflows/, and the server module that exposes agents as MCP servers via FastMCP. The codebase is well-structured with clean abstractioons (ContextDependent, AugmentedLLM base), extensive testing (over 100 test files), and comprehensive observability through OpenTelemetry tracing, token counting, and structured logging. Use cases range from simple chat agents to complex research assistants, real estate analyts, and customer service desks with swarm routing. The framework is actively maintained with frequent commits, a large community, and is built to production standards.
MCP-UI is a monorepo SDK implementing the MCP Apps standard for rendering interactive UIs over the Model Context Protocol. It provides server-side tools (in TypeScript, Python, and Ruby) to create UI resources, and a client-side React SDK to render them in sandboxed iframes. The architecture centers on a secure double-iframe proxy pattern that isolates third-party HTML and external URLs, with JSON-RPC communication via AppBridge. The codebase is well-structured with comprehensive tests, semantic release automation, and cloudflare workers example, demonstrating strong engineering practices across multiple languages.
This repository provides a TypeScript SDK for the Dedalus API and an MCP (Model Context Protocol) server that exposes the API as MCP tools. The SDK is generated from an OpenAPI specification using Stainless, offering type-safe access to AI model functionalities including chat completions, embeddings, image generation, and audio processing. It includes features such as automatic retries with exponential backoff, streaming responses, file upload handling, and structured output parsing via Zod schemas. The MCP server implementation allows Dedalus API endpoints to be used as tools by MCP-compatible clients like Claude and Cursor. It includes a compatibility layer that transforms JSON schemas to accommodate various client limitations (e.g., handling unions, inlining references, truncating tool names). The server supports both stdio and HTTP transports and can be extended with custom tools. Key architectural elements include a resource-based client structure, a robust request/response transformation system, and a modular MCP server design. The codebase demonstrates strong engineering practices with comprehensive testing, active maintenance, and clear separation of concerns between the SDK and server components.