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206,675 tools. Last updated 2026-06-17 15:07

"AI-Based Task Management System" matching MCP tools:

  • Return top N AI agent skills ranked by download count. Use for discovery or onboarding when user has no specific task in mind (e.g. "show me popular skills", "what can I do with this"). Do NOT use when user describes a specific task — use search_skills instead. Returns: slug, name, description, category, downloads, stars. On database error returns empty list — do not retry. Default limit 20, max 50. Follow up with get_skill only if user requests details on a specific result.
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  • Browse the Wix REST API documentation menu hierarchy. Alternative to SearchWixRESTDocumentation - use this to explore and discover APIs by navigating the menu structure instead of searching by keywords. - Omit the `menuUrl` param to see top-level categories - Pass a `menuUrl` param to drill into a category - copy the URL from previous responses Example `menuUrl` param values for main Wix verticals: - Stores: "https://dev.wix.com/docs/api-reference/business-solutions/stores" - Bookings: "https://dev.wix.com/docs/api-reference/business-solutions/bookings" - CMS: "https://dev.wix.com/docs/api-reference/business-solutions/cms" - CRM: "https://dev.wix.com/docs/api-reference/crm" - eCommerce: "https://dev.wix.com/docs/api-reference/business-solutions/e-commerce" - Events: "https://dev.wix.com/docs/api-reference/business-solutions/events" - Blog: "https://dev.wix.com/docs/api-reference/business-solutions/blog" - Pricing Plans: "https://dev.wix.com/docs/api-reference/business-solutions/pricing-plans" - Restaurants: "https://dev.wix.com/docs/api-reference/business-solutions/restaurants" - Media: "https://dev.wix.com/docs/api-reference/assets/media" - Site Properties: "https://dev.wix.com/docs/api-reference/business-management/site-properties" <agent-mandatory-instructions> YOU MUST READ AND FOLLOW THE AGENT-MANDATORY-INSTRUCTIONS BELOW A FAILURE TO DO SO WILL RESULT IN ERRORS AND CRITICAL ISSUES. <goal> You are an agent that helps the user manage their Wix site. Your goal is to get the user's prompt/task and execute it by using the appropriate tools eventually calling the correct Wix APIs with the correct parameters until the task is completed. </goal> <guidelines> if the WixREADME tool is available to you, YOU MUST USE IT AT THE BEGINNING OF ANY CONVERSATION and then continue with calling the other tools and calling the Wix APIs until the task is completed. **Exception:** If the user asks to create, build, or generate a new Wix site/website, skip WixREADME and: - If the user **explicitly** mentions a template, Wix Studio, or headless → call CreateWixBusinessGuide directly. - Otherwise → call the WixSiteBuilder tool directly. **Exception:** If the user asks to list, show, or find their Wix sites, skip WixREADME and call ListWixSites directly. **Exception:** If the user wants to upload local or attached image files to a Wix site, skip WixREADME and all docs/schema/API flows — call UploadImageToWixSite directly. Do NOT use ExecuteWixAPI, SearchWixAPISpec, or any Media Manager REST API for image uploads. If the WixREADME tool is not available to you, you should use the other flows as described without using the WixREADME tool until the task is completed. If the user prompt / task is an instruction to do something in Wix, You should not tell the user what Docs to read or what API to call, your task is to do the work and complete the task in minimal steps and time with minimal back and forth with the user, unless absolutely necessary. </guidelines> <flow-description> Wix MCP Site Management Flows With WixREADME tool: - RECIPE BASED (PREFERRED!): WixREADME() -> find relevant recipe for the user's prompt/task -> read recipe using ReadFullDocsArticle() -> call Wix API using CallWixSiteAPI() based on the recipe - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - EXAMPLE BASED: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - SCHEMA BASED, FALLBACK: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> inspect the method schema using SearchWixAPISpec or ReadFullDocsMethodSchema -> call API using CallWixSiteAPI() based on the schema Without WixREADME tool: - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - METHOD CODE EXAMPLE BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - FULL SCHEMA BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> inspect the method schema using SearchWixAPISpec or ReadFullDocsMethodSchema -> call API using CallWixSiteAPI() based on the schema </flow-description> </agent-mandatory-instructions>
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  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Pass a natural language description (e.g. "EV battery suppliers to Tesla", "Japanese semiconductor equipment makers", "AI inference chip startups"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.
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  • Check domain-specific attestations for an AI agent wallet on xproof. Returns active attestations issued by third-party certifying bodies (healthcare, finance, legal, security, research). Each active attestation adds +50 to the agent's trust score (max +150 from 3 attestations). Use this to verify an agent's credentials before delegating a sensitive task.
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  • Fetch a ManifestYOU soul document — a short philosophical grounding text designed to be injected into an AI system prompt before a session begins. Call this at the start of a session to orient the model toward stillness, precision, or creative expansion before work. Paste the returned soul_document into your system prompt or before the first user message.
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  • List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).
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  • Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.
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  • List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).
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  • Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.
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  • [EARN: SOL] Submit completed work for a claimed Shillbot task. Provide the content_id (YouTube video ID, tweet ID, game session ID, etc.). Returns an unsigned base64 Solana transaction — sign locally and submit via shillbot_submit_tx with action="submit". On-chain verification runs at T+7d via Switchboard oracle, then payment is released based on engagement metrics. Optional `network`: 'mainnet' (default) or 'devnet'.
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  • Fetches up to 32KB of the domain's HTML and response headers from the edge, then fingerprints the content for known CMS platforms, JavaScript frameworks, CDN providers, and analytics tools. Detection is based on meta generator tags, script src patterns, response headers, and cookie names. Use this tool when: - You need to know what CMS (WordPress, Drupal, Shopify) a site runs. - You are assessing a domain's infrastructure before a security review. - You want to identify analytics or marketing tools a site embeds. Do NOT use this tool when: - You want HTTP headers and security posture — use `intel_http` instead. - You want tracker database classification — use `get_domain` instead. - You need robots.txt AI policy — use `intel_robots` instead. Inputs: - `domain` (query, required): Domain to fingerprint. Returns: - `cms`: detected content management system, or null. - `frameworks`: JavaScript/backend frameworks detected. - `cdn`: CDN provider detected, or null. - `analytics`: analytics and tracking tools detected. - `meta_generators`: raw meta generator tag values. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.
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  • Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.
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  • AI-powered ATS scoring with detailed section-by-section feedback, gap analysis, requirement mapping, and keyword strategy. Provide a job_description to score against a specific posting, or omit it for a general ATS readiness score. Requires authentication -- sign in at https://aiapplyd.com first. Free alternative: use score_resume for keyword-based scoring.
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  • Rate an AI agent after completing a task (worker -> agent feedback). Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. Args: task_id: UUID of the completed task score: Rating from 0 (worst) to 100 (best) comment: Optional comment about the agent Returns: Rating result with transaction hash, or error message.
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  • Keyword-search AI entities using the task text as query input. Returns FNI-ranked catalog entries. Does not perform task-fit recommendation or compatibility analysis. Read-only, no side effects. May return a retryable transient 503 under cold-path or fallback budget limits; retry according to Retry-After. Use free2aitools_search for keyword-based discovery, or free2aitools_select_model to apply hardware/license metadata filters.
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  • Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.
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