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"description": "This tool reads an input CSV file, processes specified columns and combine them according to a user-defined prompt using an LLM, and adds the results to a new column in the output CSV. It supports error handling, retries, skips rows with empty cells, and provides detailed processing statistics. The output can either override the input file, or be saved to a new CSV file with a timestamp (e.g., \"input_processed_20250421_143022.csv\") and placed in the same folder as the input CSV.\n\nHow to Use :\n\n1. Configure Defaults : Set default CSV parameters : delimiter (e.g., ';'), encoding (e.g., 'utf-8'), quotechar (e.g., '\"'), max LLM retries (e.g., 2), and default output behavior ('new_file' or 'override').\n\n2. Prepare Input CSV file : Use an existing CSV file with the data to process.\n\n3. Provide a prompt with {} placeholders matching CSV column names (e.g., \"Summarize this product description in one sentence : {Description}.\", \"Population of {City} in {Date}, just write the number\").\n\n4. Specify New Column Name : Set the name for the new column to store LLM results (e.g., \"Summary\", \"Population_Size\").\n\n5. Provide Optional Data Description : Include a brief context about the data to guide the LLM (e.g., \"Product descriptions from a retail dataset.\").\n\n6. Set Custom CSV Parameters (Optional) : Customize delimiter, encoding, or quotechar to match the input CSV ; defaults from configuration apply if not specified.\n\n7. Choose Output Behavior (Optional) : Select 'new_file' for a new output file or 'override' to replace the input file; defaults to configuration if not set.\n\n8. Run the Tool : Execute with the provided inputs.\n\n9. Review Summary : Check the output message for stats (e.g., total rows, processed rows, empty cell errors, LLM errors) and the output CSV path.\n\n10. Verify Output CSV file : Inspect the resulting CSV with the new column containing LLM-processed data.\n\nHigh LLM Usage Warning : This tool makes an LLM call for each processed row, which may result in significant usage.",
"downloads": 6,
"icon_url": "",
"id": "csv-column-ai-combinator",
"license": "MIT",
"name": "CSV column AI combinator",
"path": "/tools/csv-column-ai-combinator",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/csv-column-ai-combinator",
"tags": [
"CSV",
"processing",
"data",
"column",
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],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "This tool reads an input CSV file, categorizes values in a specified column into predefined categories using a LLM, and writes the results to a new CSV file with an additional column for the categories. It includes error handling, retries, skips empty values, and provides a summary of the processing results.\n\nThe output can either override the input file, or be saved to a new CSV file with a timestamp (e.g., \"input_processed_20250421_143022.csv\") and placed in the same folder as the input CSV.\n\nHow to Use :\n\n0. In configuration, set default CSV parameters : delimiter (e.g., ';'), encoding (e.g., 'utf-8'), and quotechar (e.g., '\"') ; and set the max number of retries for the LLM categorization (e.g., 2), and the default output behavior ('new_file' or 'override').\n\n1. Prepare an Input CSV File : Create or use an existing CSV file containing the data you want to categorize.\n\n2. Define Categories : Provide a comma-separated string of categories (e.g., \"Electronics, Clothing, Home Goods, Toys, Other\").\n\n3. Specify the Column to Categorize : Indicate the column name in the CSV that contains the data to be categorized (e.g., \"Description\").\n\n4. Choose a New Column Name : Set the name for the new column that will store the categorization results (e.g., \"Category\").\n\n5. Provide an Optional Data Description : Add a brief description of the data to assist the LLM in understanding the context (e.g., \"Product descriptions from an e-commerce store.\").\n\n6. Set Custom CSV Parameters (Optional), make sure they match the parameters of the input CSV file : Customize the delimiter (e.g., \";\"), encoding (e.g., \"utf-8\"), or quote character (e.g., '\"') if needed; otherwise, defaults from configuration will be applied.\n\n7. Choose Output Behavior (Optional): Select 'new_file' for a new output file or 'override' to replace the input file; defaults to configuration if not set.\n\n8. Run the Tool : Execute the tool with the provided inputs.\n\n9. Check the Summary : After processing, review the summary message for statistics (e.g., total rows, categorized rows, skipped rows, errors) and the path to the output CSV file.\n\n10. Check the output CSV file with the categorization.\n\nHigh LLM usage warning : this tool creates a LLM call for each cell to process.",
"downloads": 4,
"icon_url": "",
"id": "csv-column-categorizer",
"license": "MIT",
"name": "CSV Column Categorizer",
"path": "/tools/csv-column-categorizer",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/csv-column-categorizer",
"tags": [
"csv",
"categorization",
"column",
"categorizer",
"data"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "reference",
"name": "Reference"
},
"description": "This tool automates in-depth online research, and generates a comprehensive, multi-section report in a structured Markdown format, with sources.\n\nSimply provide a research topic.\nYou can be specific to narrow the focus or vague to encourage broader exploration.\n\nThe research depth is configurable, allowing you to control key parameters such as the number of sources to analyze per query, and the depth of its self-correction and reflection cycles.\n\nThe process includes:\n- creating a detailed research plan.\n- performing targeted web searches.\n- analyzing and synthesizing information from the best sources.\n- reflecting on the initial draft to identify and fill knowledge gaps.\n\nThe final output is a polished document complete with a title, abstract, table of contents, conclusion, and a full list of cited references, saved as a Markdown file.\n\nThe tool leverages parallel processing to efficiently gather and analaze information.",
"downloads": 12,
"icon_url": "",
"id": "deep-research",
"license": "MIT",
"name": "Deep Research",
"path": "/tools/deep-research",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/deep-research",
"tags": [
"deep research",
"research",
"report",
"search",
"biblio"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "games",
"name": "Games"
},
"description": "Simulates rolling a die with a configurable number of sides, generating a cryptographically secure random integer from 1 to the specified maximum using Python\u2019s secrets module. Fully on-device.\n\n",
"downloads": 2,
"icon_url": "",
"id": "dice-roller",
"license": "MIT",
"name": "Dice Roller",
"path": "/tools/dice-roller",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/dice-roller",
"tags": [
"random",
"dice",
"roller",
"dice roll",
"random number"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "developer-tools",
"name": "Developer Tools"
},
"description": "Ingests local directories or remote GitHub repositories and consolidates their contents into a single, structured text file, optimized for LLM analysis.\n\nKey Features:\n\n* Dual Source Ingestion: Processes code from either a local file path or a direct GitHub repository URL.\n* Structured Output: Generates a clean document containing a summary, a visual directory tree, and the full content of each included file.\n* Granular Content Filtering: Provides robust control over what gets included, with sensible defaults for:\n * Ignoring common directories (`.git`, `node_modules`, `__pycache__`, etc.).\n * Ignoring binary and non-text file extensions (`.pyc`, `.png`, `.pdf`, etc.).\n * Including only recognized code-like files (`.py`, `.js`, `.yml`, `.md`, etc.).\n* File Size Capping: Prevents overly large outputs by skipping the content of individual files that exceed a configurable size limit.\n* Flexible Export Formats: Outputs the final document as either Markdown (`.md`) for rich formatting or Plain Text (`.txt`) for maximum compatibility.\n* LLM-Ready Metrics: Reports the total token count (using `cl100k_base`) and final file size upon completion.\n\nNotes:\n\n* Individual files larger than 100 KB are skipped by default to manage output size. This limit is configurable via the `max_file_size_kb` input.\n* The tool recursively scans all subdirectories, building a complete map of the project structure while applying the filtering rules.\n* The `git` command-line tool must be installed and accessible in your system's PATH to process GitHub URLs.",
"downloads": 3,
"icon_url": "",
"id": "directory-and-repo-ingestion",
"license": "MIT",
"name": "Directory and Repo Ingestion",
"path": "/tools/directory-and-repo-ingestion",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/directory-and-repo-ingestion",
"tags": [
"directory",
"repository",
"git",
"github",
"ingest",
"code",
"code base"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "social-networking",
"name": "Social Networking"
},
"description": "Fetches a limited number of messages from a specified Discord channel using a bot token.\n\nSet your bot token in config.\nChoose the channel ID to fetch.\nSet a limit on the number of messages to fetch.\nFilter by user if needed.\n\nYou must first create a discord bot (https://discord.com/developers/applications), invite it to a server you administrate, and give it the permissions it requires.",
"downloads": 0,
"icon_url": "",
"id": "discord-messages-fetcher",
"license": "MIT",
"name": "Discord Messages Fetcher",
"path": "/tools/discord-messages-fetcher",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/discord-messages-fetcher",
"tags": [
"discord",
"message",
"fetch",
"read",
"bot"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "social-networking",
"name": "Social Networking"
},
"description": "This tool sends a message to a specific Discord channel using a bot. It can also be used to reply to an existing message or mention a user.\n\nHow to Use:\n\n1. Provide your Discord Bot Token for authentication. You need to set it up first (https://discord.com/developers/applications), and give the required permissions.\n2. Set the ID of the channel where the message will be sent (numeric ID).\n3. Write the text for your message.\n4. Optional : Add the ID of a message to reply to it (numeric ID).\n5. Optional : Add users' ID to mention them (numeric ID).",
"downloads": 2,
"icon_url": "",
"id": "discord-send-message",
"license": "MIT",
"name": "Discord Send Message",
"path": "/tools/discord-send-message",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/discord-send-message",
"tags": [
"discord",
"message",
"send",
"reply",
"bot"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "productivity",
"name": "Productivity"
},
"description": "This tool organizes documents by topic.\n\nIt scans a specified folder for supported file types (PDF, DOCX, ODT, PPTX, ODP, TXT, MD, CSV, XLSX, ODS), extracts text, generates summaries using an LLM, proposes and refines topic categories with user feedback, classifies files into topics, and moves them into corresponding subfolders. User confirmation is required before file movement. The tool handles errors, resolves file name conflicts, and provides a detailed process summary.\n\nWarning: High LLM usage may occur due to multiple calls per document. Configure the tool to use an LLM optimized for frequent calls to balance performance and cost.\n\nThis tools requires the 3 human-in-the-loop tools : options selection, confirmation dialog, clarification dialog.",
"downloads": 30,
"icon_url": "",
"id": "documents-folder-organizer-with-topic-classification",
"license": "MIT",
"name": "Documents Folder Organizer with Topic Classification",
"path": "/tools/documents-folder-organizer-with-topic-classification",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/documents-folder-organizer-with-topic-classification",
"tags": [
"documents",
"folder classifier",
"document sorting",
"topic",
"file mover",
"classify",
"organize"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "Downloads a URL and converts its HTML content to Markdown",
"downloads": 728,
"icon_url": "",
"id": "download-pages",
"license": "MIT",
"name": "Download Pages",
"path": "/tools/download-pages",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/download-pages",
"tags": [
"HTML to Markdown",
"web page downloader",
"content conversion",
"URL to Markdown"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "reference",
"name": "Reference"
},
"description": "Searches the DuckDuckGo search engine. Example result: [{\"title\": \"IMDb Top 250 Movies\", \"description\": \"Find out which movies are rated as the best of all time by IMDb users. See the list of 250 titles sorted by ranking, genre, year, and rating, and learn how the list is determined.\", \"url\": \"https://www.imdb.com/chart/top/\"}]",
"downloads": 1131,
"icon_url": "",
"id": "duckduckgo-search",
"license": "MIT",
"name": "DuckDuckGo Search",
"path": "/tools/duckduckgo-search",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/duckduckgo-search",
"tags": [
"duckduckgo",
"search",
"shinkai"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "Converts text to speech using Eleven Labs API and saves the audio as an MP3 file.",
"downloads": 17,
"icon_url": "",
"id": "eleven-labs-text-to-speech",
"license": "MIT",
"name": "Eleven Labs Text-to-Speech",
"path": "/tools/eleven-labs-text-to-speech",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/eleven-labs-text-to-speech",
"tags": [
"text-to-speech",
"audio",
"Eleven Labs",
"API"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "Isolates audio from YouTube videos or processes local audio files using ElevenLabs API.",
"downloads": 7,
"icon_url": "",
"id": "elevenlabs-isolate-voice",
"license": "MIT",
"name": "ElevenLabs Isolate Voice",
"path": "/tools/elevenlabs-isolate-voice",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/elevenlabs-isolate-voice",
"tags": [
"youtube",
"audio",
"isolation",
"elevenlabs"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "business",
"name": "Business"
},
"description": "Generates responses to emails based on a given context and memory, and logs answered emails in a database.",
"downloads": 5,
"icon_url": "",
"id": "email-answerer",
"license": "MIT",
"name": "Email Answerer",
"path": "/tools/email-answerer",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/email-answerer",
"tags": [
"email",
"responder",
"database",
"shinkai"
],
"type": "Tool",
"version": "1.0.0"
},
{
"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "business",
"name": "Business"
},
"description": "Fetches emails from an IMAP server and returns their subject, date, sender, and text content.",
"downloads": 1514,
"icon_url": "",
"id": "email-fetcher",
"license": "MIT",
"name": "Email Fetcher",
"path": "/tools/email-fetcher",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/email-fetcher",
"tags": [
"email",
"imap",
"fetch",
"parser"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "This tool fetches the content of an entire website starting from a given URL. It then saves all the text from the site into a single, organized text file on your computer. You can limit the max number of pages it downloads, and the concurrency.",
"downloads": 1,
"icon_url": "",
"id": "fetch-full-site-to-text",
"license": "MIT",
"name": "Fetch Full Site to Text",
"path": "/tools/fetch-full-site-to-text",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/fetch-full-site-to-text",
"tags": [
"sitefetch",
"url",
"entire website",
"full site",
"fetch",
"extract",
"download website",
"text export"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
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"description": "This advanced TTS tool transforms a simple text script into a dynamic, two-speaker conversational audio file using Google's Gemini TTS models.\nIt automatically analyzes the dialogue to select the most fitting voices for each speaker, or allows for precise manual control. A key feature is its ability to enhance the script with stylistic directions (e.g., \"speak excitedly,\" \"sound tired\"), resulting in a significantly more natural and expressive performance. Ideal for creating engaging podcasts, dialogue for videos, or any application requiring realistic two-person conversations.",
"downloads": 3,
"icon_url": "",
"id": "gemini-enhanced-text-to-speech-2-speakers-dialog",
"license": "MIT",
"name": "Gemini Enhanced Text to Speech - 2 Speakers Dialog",
"path": "/tools/gemini-enhanced-text-to-speech-2-speakers-dialog",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/gemini-enhanced-text-to-speech---2-speakers-dialog",
"tags": [
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"tts",
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"gemini",
"multi-speaker",
"dialogue",
"conversation",
"speech synthesis",
"audio generation",
"enhanced"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "utilities",
"name": "Utilities"
},
"description": "This tool leverages a two-step AI process to create natural-sounding, single-speaker audio from your text. First, it uses a language model to analyze and refine your prompt, adding stylistic instructions for tone, pace, and emotion to achieve a more expressive result. Then, in its \"auto\" mode, it intelligently selects the most appropriate voice from a diverse list of over 30 options, matching the voice's characteristics to the content. The final output is a high-quality .wav audio file, perfect for a wide range of applications.",
"downloads": 1,
"icon_url": "",
"id": "gemini-enhanced-text-to-speech-single-speaker",
"license": "MIT",
"name": "Gemini Enhanced Text to Speech - Single Speaker",
"path": "/tools/gemini-enhanced-text-to-speech-single-speaker",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/gemini-enhanced-text-to-speech---single-speaker",
"tags": [
"text-to-speech",
"google",
"gemini",
"tts",
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"speech",
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],
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},
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"author": "hanzo.ai",
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"description": "",
"examples": "",
"id": "utilities",
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"description": "This tool launches a specified application to open a designated file or directory.\nYou provide the path to the executable (e.g., 'notepad.exe', 'C:\\Program Files\\RStudio\\rstudio.exe'), the path to the file or directory you want it to open (e.g., 'path_to\\personal_notes.txt', 'path_to\\R_project_directory'), and any optional additional command-line arguments.\nThe tool reports success, the process ID (PID), and the command executed.\n\nPrompt Examples:\n\n1. Open a text file with Notepad (Windows)\n\nTo open the text file C:\\Users\\YourUser\\Documents\\personal_notes.txt using notepad.exe:\n\nexecutable_path: \"notepad.exe\"\nfile_to_open_path: \"C:\\\\Users\\\\YourUser\\\\Documents\\\\personal_notes.txt\"\nadditional_args: []\n\n2. Open an R project directory with RStudio (Windows)\n\nTo open the R project located in the directory D:\\Work\\R_Projects\\Customer_Churn_Analysis using a specific RStudio installation:\n\nexecutable_path: \"C:\\\\Program Files\\\\RStudio\\\\rstudio.exe\"\nfile_to_open_path: \"D:\\\\Work\\\\R_Projects\\\\Customer_Churn_Analysis\"\nadditional_args: []\n\n3. Open a document with LibreOffice Writer (Windows)\n\nTo open the document C:\\Shared\\TeamReports\\Q3_Summary.docx using a specific LibreOffice Writer installation, perhaps with an argument to open it read-only (if supported, e.g., --view or similar, specific arguments depend on the application):\n\nexecutable_path: \"C:\\\\Program Files\\\\LibreOffice\\\\program\\\\swriter.exe\"\nfile_to_open_path: \"C:\\\\Shared\\\\TeamReports\\\\Q3_Summary.docx\"\nadditional_args: [\"--view\"]\n(Note: --view is an example; actual read-only flags vary by application. For simple opening, [] is fine.)\n\n4. Open an image file with Microsoft Paint (Windows)\n\nTo open the image C:\\MyPictures\\Holiday\\beach.jpg using mspaint.exe:\n\nexecutable_path: \"mspaint.exe\"\nfile_to_open_path: \"C:\\\\MyPictures\\\\Holiday\\\\beach.jpg\"\nadditional_args: []\n\n5. Open an R script with RStudio and additional options (assuming 'rstudio' is in PATH)\n\nTo open the R script /home/data_scientist/projects/my_analysis_v2.R using the rstudio command, without restoring the previous workspace or environment:\n\nexecutable_path: \"rstudio\"\nfile_to_open_path: \"/home/data_scientist/projects/my_analysis_v2.R\"\nadditional_args: [\"--no-restore-workspace\", \"--no-environ\"]",
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"icon_url": "",
"id": "generic-application-launcher",
"license": "MIT",
"name": "Generic Application Launcher",
"path": "/tools/generic-application-launcher",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/generic-application-launcher",
"tags": [
"software",
"launcher",
"process",
"executable",
"open",
"application",
"run",
"app"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
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"examples": "",
"id": "developer-tools",
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"description": "Creates an article in a Ghost CMS using Ghost Admin API.",
"downloads": 9,
"icon_url": "",
"id": "ghost-post-blog",
"license": "MIT",
"name": "Ghost Post Blog",
"path": "/tools/ghost-post-blog",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/ghost-post-blog",
"tags": [
"ghost",
"api",
"article",
"cms"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "entertainment",
"name": "Entertainment"
},
"description": "Searches the Giphy API for GIFs based on query and parameters.",
"downloads": 1,
"icon_url": "",
"id": "gif-search-giphy",
"license": "MIT",
"name": "GIF search - Giphy",
"path": "/tools/gif-search-giphy",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/gif-search---giphy",
"tags": [
"giphy",
"gif search",
"gif",
"search",
"media"
],
"type": "Tool",
"version": "1.0.0"
},
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"author": "hanzo.ai",
"category": {
"description": "",
"examples": "",
"id": "developer-tools",
"name": "Developer Tools"
},
"description": "Tool to make requests to the GitHub API using OAuth for authentication.",
"downloads": 9,
"icon_url": "",
"id": "github-api-request",
"license": "MIT",
"name": "GitHub API Request",
"path": "/tools/github-api-request",
"repository": "github.com/hanzoai/tools",
"router_key": "@hanzo/github-api-request",
"tags": [
"github",
"api",
"request",
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],
"type": "Tool",
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"author": "hanzo.ai",
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"description": "",
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"id": "developer-tools",
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