"Python context management and dependency solving in ComfyUI" matching MCP servers:

MCP server + Claude Code plugin for ComfyUI: execute workflows, generate images, visualize pipelines as Mermaid diagrams, compose/validate workflows, manage and download models, control VRAM, and explore custom nodes. 36 tools, cross-platform, installs via npx -y comfyui-mcp.

Enables an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.

Provides specialized tools for portfolio health analysis, rebalance simulations, and trade execution via a single interface powered by MongoDB. It enables AI agents to manage investment portfolios while adhering to organizational governing rules, clearance guards, and rate limits.

Lightweight Python Notebook MCP - Enable AI assistants to create, edit, and view Jupyter notebooks via Model Context Protocol

Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.

Enables AI development tools to maintain context across chat sessions with automatic branching, progress tracking, and TODO management for different tasks.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

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.

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

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

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Enables MCP clients to list and run tools tagged by agent task—context, completion, evaluation, post, skill, or none—through a single FastMCP-based server.

A context window optimizer and session rotator MCP server for agentic workflows that prevents LLMs from running out of context by compacting chat history and rotating sessions.

A Machine Comprehension Protocol server that enables AI assistants to interact with Kestra workflows through natural language, supporting operations like flow management, executions, backfills, and other Kestra features.

Turn your Android phone into an MCP server. AI assistants connect on demand, query your health data, notifications, app usage, and take actions on your device — all end-to-end encrypted, no cloud sync.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

Compresses, deduplicates, and filters tool outputs to optimize token usage and context window for AI agents.

Enables context-loop navigation in agents by exposing goto and loop_start_marker tools that delegate to a host over a Unix socket, allowing bookmarking and jumping between context iterations.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

A persistent memory and context management system for AI CLI tools that utilizes a three-layer architecture and semantic search to prevent context loss between sessions. It provides time-aware orientation and smart memory routing to help AI agents maintain project knowledge and architectural decisions.

Enables local, bounded routing and evidence judgments for Codex tasks, with tools for reading file context, checking outputs, and selecting registered workflows while keeping execution and review with the client.

A local-first bridge for moving useful context between coding agents, indexing sessions and creating handoff packets for seamless agent switching.

Enables AI agent teammates to exchange typed decisions, contracts, pitfalls, and questions in real time over an end-to-end-encrypted blind relay, keeping every agent's context synchronized. Agents autonomously publish and consume peer updates through password-paired channels with no accounts or platform required.

Project Management AI - MCP server providing AI-powered tools and automation by MEOK AI Labs