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

A Model Context Protocol server for checking package versions and metadata across multiple package managers including npm, PyPI, Maven, NuGet, RubyGems, Crates.io, and Go modules.

An MCP server that provides cross-validated npm dependency fitness verdicts, including deprecation, malicious detection, and safe migration target inference.

MCP server for ComfyUI — text-to-image, variations, img2img refine, upscale, image proxy, and workflow runner.

MCP server + Claude Code plugin for ComfyUI — execute workflows, generate images, visualize pipelines, manage models, control VRAM, and explore custom nodes, all from your AI coding assistant.

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.

MCP server that lets AI agents drive a remote ComfyUI instance end-to-end, including queueing workflows, viewing outputs, downloading models, and authoring graphs via ComfyScript.

Checks npm and PyPI packages for outdated versions, deprecation status, and breaking changes with cited sources, enabling AI agents to verify dependency freshness.

MCP server for amazon.in — cheapest in-stock, best-value picks, and price history links.

Enables Claude Desktop to interact with local ComfyUI installations for AI-powered image generation, including workflow management, model selection, real-time monitoring, and custom workflow execution through natural language.

Enables scanning repositories for hardcoded service URLs and clients, and building service-to-service API dependency maps.

Enables security scanning for npm dependencies by checking manifest and lockfiles against the OSV.dev and Socket.dev vulnerability databases. It provides tools to detect vulnerabilities in specific packages and retrieve detailed technical reports for identified security issues.

Dynamically loads ComfyUI workflows as MCP tools, enabling AI assistants to generate images, videos, and audio by executing workflows across categories like text-to-image, image-to-video, and text-to-audio with automatic parameter mapping and progress monitoring.

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.

A lightweight MCP server that bridges AI agents with a local ComfyUI instance to generate and iteratively refine images, audio, and video through conversational tool calls.

Enables users to look up package versions, scan for vulnerabilities, and analyze dependencies across multiple registries (npm, Maven, PyPI, etc.) using exact version recommendations for security.

A lightweight MCP server that bridges AI agents with a local ComfyUI instance, enabling them to generate and iteratively refine images, audio, and video through conversational tool calls.

Enables AI agents to generate images and videos through natural language by exposing ComfyUI workflows as parameter-configurable MCP tools.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Local-first documentation for AI agents

Enables AI agents to submit npm, PyPI, Go, or Maven packages and manifests to the free OSV.dev database and receive per-package and per-advisory results with CVE mappings, CVSS scores, severities, and the versions that fix each issue. It also supports filtering by minimum severity, de-duplicating alias advisories, and machine-readable error/warning rows for CI gates and upgrade recommendations.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

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.

Connects AI assistants to your n8n instance to manage workflows, executions, credentials, and user settings through 31 specialized tools. It enables users to perform n8n operations like workflow creation, activation, and execution history retrieval via natural language.

Integrates Azure Cost Management APIs via MCP, enabling cost queries, forecasts, budgets, and Advisor recommendations through natural language in compatible clients.

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.

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.

Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida