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"Improving and Refining Prompts for Various Uses" matching MCP servers:

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    A tutorial demonstrating how to build reusable, parameterized MCP prompt templates using FastMCP, including patterns for research, summarization, and code review.
    2
    MIT
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    Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.
    52
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    Apache 2.0
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    A reference MCP server that enforces identity-aware tool access using role-based permissions (Reader, Analyst, Auditor) and supports multiple identity routes like Entra RBAC, Copilot Studio, and Okta XAA. It provides read-only access to test evidence with explicit denial paths for unauthorized tools.
    7
    MIT
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    An MCP server that lets AI assistants like Claude read LinkedIn data through your own logged-in browser session. Access profiles and companies, search for jobs, or get job details.
    17
    Apache 2.0
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    MCP server for structured Australian macroeconomic and financial data from the Australian Bureau of Statistics (ABS), the Reserve Bank of Australia (RBA), and the Australian Prudential Regulation Authority (APRA).
    4
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    MIT
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    A comprehensive MCP server for interacting with the Civitai API to search models, browse images with generation prompts, and manage downloads. It features extensive support for various base models like SDXL and Flux, includes NSFW filtering, and provides automated download commands for ComfyUI.
    8
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    MIT
  • F
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    A small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.
    6
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    A standalone MCP Tool Registry server providing generic tool search and discovery for MCP clients, reducing token consumption and improving tool selection accuracy.
    6
    MIT
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    Analyzes, refines, and optimizes prompts for AI assistants by fixing grammar, improving clarity, applying best practices like chain-of-thought and few-shot learning, and scoring prompt quality across multiple dimensions.
    3
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    MIT