"Understanding Prompts or Prompt Engineering" matching MCP servers:

Enables searching for high-quality AI art prompts from Banana Prompts by tags or keyword queries, with direct links to full prompts and images.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

Manages AI prompts stored in Notion databases, enabling search, retrieval, and management across multiple prompt collections.

A Model Context Protocol implementation for managing and serving AI prompts with a TypeScript-based architecture in a monorepo structure.

Enables clients to optimize prompt prefix caching by matching longest common token prefixes with a radix trie, eliminating redundant prefill computation and reducing time-to-first-token.

Compresses multi-turn conversation history by stripping verbose boilerplate and filler tokens while preserving critical tool parameters and named entities, deterministically cutting inference costs by 40-60%. Runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and other MCP runtimes.

Enables MCP-compatible clients to deterministically neutralize adversarial prompt injections and jailbreak overrides with zero-dependency, sub-millisecond heuristic analysis.

Enables deterministic detection and neutralization of adversarial prompt injections and override attempts in AI agent workflows via a zero-dependency MCP server, providing structured telemetry and low-latency validation.

Enables autonomous AI agents and MCP-compliant clients to detect and block adversarial prompt injections, jailbreak attempts, system prompt exfiltration, and tool hijacking via a zero-dependency, sub-millisecond heuristic firewall.

A remote MCP server exposing 3 tools via Model Context Protocol: score_prompt (free, grades any LLM prompt A-F on 0-40 scale), optimize_prompt (returns optimized prompt + dimension breakdown, $0.025 USDC), and compare_models (Claude vs GPT-4o head-to-head, $1.25 USDC). Remote HTTP server at /api/mcp.

Facilitates access and management of Langfuse prompts through the Model Context Protocol, enabling prompt discovery, retrieval, and integration within clients like Claude Desktop and Cursor.

MCP server for IBM Concert Operate that exposes the full v2 REST API as callable tools, enabling AI assistants to manage alerts, incidents, policies, runbooks, and more.

Enables querying Keypup engineering analytics (delivery, quality, team workload) using natural language through MCP-compatible AI assistants.

Zero-Ops deploy of a private AI coding workspace to your own VPS, straight from your AI chat. Registers the user, recommends a VPS, and runs and monitors the full deployment.

Free MCP tools for context engineering audits of AI agents, offering on-demand analysis of system prompts, tool definitions, and context packing.

Enables AI agents to interact with Freeplay, an ops platform for AI engineering teams, to analyze production logs, identify quality issues, iterate on prompts and agents using real data, and run experiments before deploying.

Protects AI agents from prompt injection and destructive actions while enabling self-improving prompts through a security pipeline.

Enables creating diagrams or generating code from diagrams in StarUML via prompts.

Integrates AI safety analysis, red-teaming, and prompt auditing directly into MCP-compatible clients like Claude Desktop and Cursor IDE, allowing real-time analysis of prompts and detection of jailbreak attempts.

Enables AI coding agents to automatically lint and auto-fix their generated prompts before running them, helping improve prompt quality, security, and cost efficiency.