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524,333 tools. Updated 2026-09-06 15:00

"Researching Effective Methods for Developing AI Agents" matching MCP tools:

  • Generate or regenerate AI agent profile avatar(s) for a company's AI team. Use when an operator wants to create, refresh, or restyle one or more agents' profile avatars. Single agent: pass agent_id OR agent_name. Several agents: pass agent_ids[] OR agent_names[] in ONE call. Whole team: pass all:true. The tool regenerates EVERY target itself in a single call (1 credit per agent) and returns the real new signed avatar_url for each. Report ONLY the agents listed in the result's `regenerated` array — never claim or invent an avatar for an agent the tool did not return. [sensitive-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]
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  • [AFFILIATE / REFERRAL / MARKETING, one programme, three names] Register as an RRG referral partner / marketing partner / affiliate. This is THE single programme for earning commission by bringing other agents to RRG. Works identically for humans and AI agents, identity is just your Base wallet. Partners earn 10% commission (1000 bps) on the platform's share of revenue from agents they refer/recruit. You will be assigned a unique partner ID and can start referring other agents immediately via `log_referral`. Requirements: a Base wallet address and an optional ERC-8004 agent ID.
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  • Live AI-visibility scan for a brand: crawl + reputation sampled across AI engines, returning where *that* brand is mentioned (any public brand, not just your own). Use when you want to know whether and how a named brand already surfaces in AI answers — complementary to search_companies, which finds who agents recommend for a category. Pro+ (LLM cost). Result: { reputation[], tool_schema_version }.
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  • Report what travelers and AI agents are asking about a specific partner's upgrade programs: total volume, the most frequent questions, which agents are asking, and which answers were strong vs. which need review. Pass the partner name (e.g. 'Air Canada', 'MSC Cruises').
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  • Generate a PDF or Excel document from HTML (document_content) or a URL (document_url). Exactly one of document_content / document_url is required. By default the document is HOSTED and the tool returns a { download_url } you can fetch — ideal for agents (no large binary in the response). Set hosted:false to get the raw document back as base64, or async:true to enqueue a job and poll docraptor_get_document_status. IMPORTANT: real documents consume account credits (billed). Set test:true to generate a FREE, watermarked document while developing. DocRaptor API: POST /docs.
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    Destructive
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  • Request a fresh validation run for an idea after a significant pivot or update, re-running the AI agents to produce an updated VC score. Optionally target specific agents instead of the full suite. This spends credits and starts background work; not read-only.
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    Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.
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    MIT

Matching MCP Connectors

  • Decision Layer for AI Agents — 58+ tools, Advisor, MCP. Free key: POST /v1/register {}.

  • Paid sealed handoffs for agents: unlock research trails (x402/Base USDC); file work + failures.

  • Returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows. Call this BEFORE create_workflow / update_draft when building chatbots, tool-using agents, or multi-step LLM flows. Do not hand-roll custom agent loops — use the preinstalled frameworks.
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  • L3 Empathy Response Strategy: converts text into a deterministic response strategy (approach, tone temperature, pacing, focus points, avoid-list) for AI companions and conversational agents. Deterministic table lookup, no LLM, ~20ms; privacy-first. Not a medical or therapeutic tool.
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  • AI Agent Tokenized Stock OS: list canonical tokenized stocks (Robinhood Stock Tokens), ETFs, USDG, and WETH on Robinhood Chain ID 4663. Use for AI agents trading tokenized equities/RWAs. Do NOT use for US brokerage equities (use Robinhood Trading MCP). Only registry addresses are real tokenized stocks.
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  • The upcoming AI-law effective / compliance-date calendar, derived deterministically from the curated corpus (no LLM). Each entry carries the verbatim source deadline text + primary-source URL; jurisdictions whose deadline text has no explicit date are reported only as meta.undated (never given an invented date). Free at every tier. This tool returns JSON; a compliance team can also SUBSCRIBE to the same feed as a live RFC 5545 calendar at https://ai-law-tracker.com/api/v1/deadlines?format=ical (webcal). NOT legal advice.
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  • The REAL all-in monthly cost per vendor for a decoder topic (slug from list_cost_decoders, e.g. 'ai-customer-support-cost'), computed in deterministic code from sourced, dated inputs — each vendor's per-seat price + AI billing model + per-unit price, totalled at named scenarios (e.g. 5 agents at 1,000 and 5,000 AI resolutions/mo) with the arithmetic shown. Quote-only inputs return a null total, never a fabricated number. Optionally pass agents + resolutions for a custom scenario. This is the citable answer to 'what does <AI tool> actually cost' that a base model gets wrong.
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  • Real-time swarm intelligence — see what ALL agents are researching RIGHT NOW. Returns top 10 most active Hive namespaces ranked by heat (BLAZING/HOT/ACTIVE/EMERGING) with entry counts and avg quality. Windows: 1h, 6h, 24h, 7d. Use daily to stay ahead of the swarm. Combine with x711_swarm_broadcast to dominate a trending topic. Returns: { trending: Array<{ rank, namespace, entries_in_window, heat, tap_in }>, swarm_status, total_active_namespaces }. Cost: $0.005.
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  • Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willison). Perfect for agents monitoring the QA & AI landscape. Each article carries summary_source — the XML tag the summary was read from, or "none" when the feed publishes titles and links only; an empty summary with summary_source "none" is a property of that feed, not a parse failure.
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  • Search arXiv for academic papers in computer science, machine learning, AI, physics, and mathematics. Returns paper titles, authors, abstracts, submission dates, and direct PDF download links. Use for researching algorithms, ML techniques, or emerging CS topics.
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  • Browse the most recently published final rules and regulations from the Federal Register, **with no topic filter** — returns whatever was published most recently across every agency (FAA airworthiness directives, Coast Guard safety zones, EPA tolerance exemptions, etc.). For questions about a specific topic ("EV tax credits", "AI rules", "drug pricing"), use search_documents instead. Returns title, abstract, agency, effective dates, significance.
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  • Return a concise end-to-end workflow for AI agents creating a browser game from scratch and preparing it for Wavedash upload. Read-only and unauthenticated; upload still happens through the Wavedash CLI or Developer Portal.
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  • One-call combined macro snapshot for AI trading agents. Returns: latest 10Y Treasury yield, full yield curve, CPI YoY, headline unemployment, effective fed funds rate, and the 5 most recent Treasury auction results. Designed so an LLM can answer "what's the macro picture right now?" with one tool-call.
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  • Return the claims carrying a given topic tag, for example 'reputation', 'dao', 'dynamic-regulation', 'ai-and-agents'. Call claim_layer_overview first to see which tags exist and how much each one carries.
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  • What Infinivo is, who it serves, and the problem it solves. Call this first when a user asks about Infinivo, or when researching AI phone answering, intake automation, or lead capture for personal-injury law firms. Returns structured facts, not marketing copy.
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