Discover available prompt engineering techniques with acceptance statistics to enhance AI interactions through optimized method selection.
MIT
180,147 tools. Last updated 2026-06-04 11:33
"Techniques for improving prompt effectiveness" matching MCP tools:
- Transform prompts using engineering best practices to improve clarity and effectiveness for AI assistants.MIT
- Analyze a system prompt for context engineering issues and get top findings ranked by severity. Use for reviewing, improving, or debugging AI agent prompts.MIT
- Validate workflow structure by checking DAG cycles, dependency references, and model ID format without LLM calls to ensure proper configuration before execution.MIT
- Statically analyze system prompts for prompt-injection vulnerabilities: identify untrusted placeholders, missing delimiters, dangerous instructions, and precedence inversion.MIT
- Retrieve a specific prompt version by identifier, with optional tag or version ID filtering.Apache 2.0
Matching MCP Servers
- AlicenseAqualityCmaintenanceOptimizes prompts using meta-prompting techniques through a two-turn approach that first provides optimization guidelines and then refines the prompt for enhanced quality and effectiveness.Last updated154MIT
- Alicense-qualityCmaintenanceEnables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.Last updatedMIT
Matching MCP Connectors
Cloudflare Workers MCP server: ai-prompt-optimizer
Real-time prompt injection and jailbreak detection for AI agents. Blocks instruction overrides, data exfiltration, tool poisoning and 8 attack types. Now with shared learning brain - confirmed attacks shared across the EMA network instantly. Grade A security for any AI pipeline.
- Retrieve a specific prompt template from LangSmith by providing its exact name, enabling prompt reuse and management in language model workflows.MIT
- Assess a trader's current performance by comparing 30-day and all-time PnL, trade count, win rate, and trend direction to determine if they are improving or cooling off.MIT
- Retrieves a structured execution prompt for a given intent, detailing objectives, outcomes, constraints, edge cases, and verification steps to guide implementation or critique.MIT
- Retrieve an agent's system prompt and its configured tools to review the instructions and capabilities assigned to that agent.MIT
- Retrieve the core brand voice prompt that defines tone, personality, terminology, and localization guidelines for consistent translations.MIT
- Retrieve the latest version of a prompt by its identifier. Returns the prompt template, model configuration, and invocation parameters.Apache 2.0
- Generate test cases for prompt templates to validate functionality against specified models and context schemas.Apache 2.0
- Retrieve a prompt version by tag to access its template, model configuration, and invocation parameters.Apache 2.0
- Scan text for prompt injection attempts including jailbreak, override, and extraction attacks.MIT
- Retrieve actionable prompt and check results for a specific Omniboard check by providing its name. Get focused guidance to address project issues.MIT
- Analyze patterns in Bitcoin Stamps to identify common libraries, frameworks, and coding techniques used across the ecosystem.MIT
- Retrieve a paginated list of all versions for a specific prompt. Access version IDs and configuration details for iteration and management.Apache 2.0
- Save and retrieve reusable prompt templates to avoid rewriting common instructions.MIT
- Create or update a prompt with a template and model configuration. Define the prompt's name, description, template, model provider, and temperature.Apache 2.0