banana-prompts-mcpofficial
F
licenseB
qualityD
maintenanceEnables searching for high-quality AI art prompts from Banana Prompts by tags or keyword queries, with direct links to full prompts and images.
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"Servers Offering MCP Prompts as Prompt Primitives" matching MCP servers:
- AlicenseBqualityBmaintenanceMCP server providing 26 visual tools for text-only LLMs, enabling description, coordinate location, OCR, annotation, cropping/zooming, anomaly scanning, and computer control with switchable VLM backends.278MIT
- FlicenseAqualityDmaintenanceEnables AI assistants to register prompts for security verification, checking for hidden injections, data exfiltration patterns, and jailbreak attempts, then generate embed codes for displaying security badges on websites.4
- FlicenseAqualityCmaintenanceMCP server for managing tasks with a resource to read all tasks, a tool to add tasks, and a prompt to analyze tasks, using stdio transport.1
- AlicenseAqualityDmaintenanceA Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.52516MIT
- Alicense-qualityCmaintenanceA tutorial demonstrating how to build reusable, parameterized MCP prompt templates using FastMCP, including patterns for research, summarization, and code review.2MIT
- Alicense-qualityCmaintenanceA small, local, network-free MCP server for the RISEN prompt-engineering framework. It gives any MCP client tools to build, validate, save, and reuse structured prompts.8MIT
- Alicense-qualityCmaintenanceLightweight MCP server for providing user prompts defined in Markdown with argument support.6BSD 3-Clause
- Alicense-qualityBmaintenanceProvides AI assistants with access to a library of 924 free ecommerce marketing prompts across 43 categories, enabling tools to list collections, search prompts, and retrieve full templates with variables and recommended AI models.MIT
- Alicense-qualityDmaintenanceAutomatically extracts actionable methodologies from articles and URLs, stores them in a vector database, and retrieves relevant methods to enhance user prompts for more effective AI interactions.4MIT
- Alicense-qualityCmaintenanceEnables creation, management, and templating of prompts through a simplified SOLID architecture, allowing users to organize prompts by category and fill in templates at runtime.83117MIT
- Alicense-qualityDmaintenanceTurns Claude-style skill folders into MCP tools, resources, and prompts for discovering and using skills.81MIT
- Alicense-qualityDmaintenanceA simple MCP server for utilizing prompts defined in YAML files.MIT
- AlicenseAqualityCmaintenanceMCP server providing on-demand access to official prompting guides for building robust meta-prompts.22MIT
- AlicenseAqualityDmaintenanceRun DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.223MIT
- FlicenseAqualityBmaintenanceDeterministic SNAP eligibility logic, exposed as auditable Model Context Protocol tools. An AI agent can read, reason, and orchestrate, but the eligibility determination itself is made by versioned, tested, cited code that a caseworker, an auditor, or a court can inspect.5
- FlicenseAqualityDmaintenanceAn enhanced MCP server that fetches prompts from GitHub repositories with intelligent discovery, composition, and management features.71
- AlicenseAqualityAmaintenanceLocal MCP server that uses a local Ollama model to rewrite rough prompt drafts into structured, optimized prompts for paid APIs, saving tokens and improving output quality.5133MIT
- AlicenseAqualityCmaintenanceEnables prompt optimization loops and regression test suites for Claude Code, with a companion web UI for real-time visualization of scores and prompt revisions.19MIT
- AlicenseAqualityDmaintenanceAn MCP server that uses Claude 3.5 Sonnet to transform ordinary prompts into structured, professionally engineered instructions for any LLM. It enhances AI interactions by adding context, requirements, and structural clarity to raw user inputs.13MIT