hemanth/paws-on-mcp

3.9

paws-on-mcp is hosted online, so all tools can be tested directly either in theInspector tabor in theOnline Client.

If you are the rightful owner of paws-on-mcp and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

Paws-on-MCP is a comprehensive Model Context Protocol (MCP) server implementing the latest MCP 2025-03-26 specification, designed to demonstrate advanced MCP capabilities.

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get_server_prompts

List all available prompt templates.

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search_hackernews

Search HackerNews stories by title. Args: query: Search term to look for in story titles limit: Maximum number of stories to return (default: 5, max: 20) Returns: List of matching stories

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get_github_repo_info

Get detailed information about a specific GitHub repository. Args: owner: Repository owner (username or organization) repo: Repository name Returns: Detailed repository information

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create_sampling_request

Create a sampling request according to MCP 2025-03-26 specification. This tool demonstrates proper MCP sampling by creating requests that clients can process to get LLM completions with server context and enhanced model preferences. Args: prompt: The prompt to send to the LLM context_data: Optional context data to include max_tokens: Maximum tokens to generate temperature: Sampling temperature (0.0 to 1.0) model_hint: Optional model name hint (e.g., "claude-3-sonnet", "gpt-4") intelligence_priority: How much to prioritize intelligence (0.0-1.0) cost_priority: How much to prioritize cost efficiency (0.0-1.0) speed_priority: How much to prioritize response speed (0.0-1.0) Returns: Properly formatted MCP sampling request per 2025-03-26 spec

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analyze_hackernews_trends_with_ai

Analyze HackerNews trends using AI through sampling. This tool demonstrates how servers can use sampling to get AI analysis of data they collect. Args: topic: Topic to analyze in HackerNews stories count: Number of stories to analyze analysis_type: Type of analysis (brief, detailed, comprehensive) Returns: AI analysis request with collected data

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code_review_with_ai

Get AI-powered code review insights for a GitHub repository. Demonstrates using sampling to get AI analysis of repository data. Args: repo_owner: GitHub repository owner repo_name: Repository name review_focus: Focus area (security, performance, architecture, general) Returns: AI code review request with repository context

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