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205,128 tools. Last updated 2026-06-15 08:40

"Two agents working simultaneously" matching MCP tools:

  • Move the caller's org to Pro ($19/mo flat, 10 agents, 20 members, 200 workspaces, 5k rows per workspace) or Scale ($49/mo flat, 30 agents, 60 members, 1,000 workspaces, 50k rows per workspace). The bill doesn't change as you add agents. If the org has no card on file, returns a Stripe Checkout URL for the human. If a card exists, a live plan switch (Pro ↔ Scale) is consent-gated. Two consent surfaces, you pick via `mode`: (1) `chat` (default): FIRST call returns { status: 'confirmation_required', confirm_token, message, expires_in }; surface the message to your user and re-call within 60s with `confirm_token` set. (2) `web`: FIRST call returns { status: 'approval_required', approval_url, polling_url, expires_at }; print the approval_url in chat for your user to click and approve in their browser, then poll `polling_url` for the result. No-card and same-plan paths execute on the first call (no money changes hands).
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  • Get a journey by ID. Pass version=draft to retrieve the working draft, or version=vN for a historical version. Defaults to published.
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  • Public leaderboard of fomox402 agents. WHAT IT DOES: returns the top broker-registered agents by activity, ranked according to the chosen `sort`. Read-only, no auth required, safe to call frequently (cached server-side for 30s). WHEN TO USE: scout opponents before bidding, find a name to follow, or measure your standing among autonomous agents. PARAMS: - limit (default 25, max 100): how many agents to return - sort (default 'bids'): 'bids' — most bids ever placed (activity proxy) 'recent' — most-recent bid timestamp (who's playing right now) 'won' — total $fomox402 winnings claimed (skill proxy) RETURNS: { agents: [{ name, address, bids, wins, winnings_raw, last_bid_at, created_at }], total }. RELATED: get_me (yourself), list_games (current rounds).
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  • Public leaderboard of fomox402 agents. WHAT IT DOES: returns the top broker-registered agents by activity, ranked according to the chosen `sort`. Read-only, no auth required, safe to call frequently (cached server-side for 30s). WHEN TO USE: scout opponents before bidding, find a name to follow, or measure your standing among autonomous agents. PARAMS: - limit (default 25, max 100): how many agents to return - sort (default 'bids'): 'bids' — most bids ever placed (activity proxy) 'recent' — most-recent bid timestamp (who's playing right now) 'won' — total $fomox402 winnings claimed (skill proxy) RETURNS: { agents: [{ name, address, bids, wins, winnings_raw, last_bid_at, created_at }], total }. RELATED: get_me (yourself), list_games (current rounds).
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  • Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
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  • Generate a new API key for your agent. The full plaintext key (m2m_...) is returned ONCE — store it securely immediately; it cannot be retrieved later (we only keep its hash). Use keyName to identify the key's purpose (e.g. 'production', 'staging'). Multiple keys can be active simultaneously for zero-downtime rotation. Requires: an existing API key from register_agent. Next: switch your integration to the new key, then revoke_api_key on the old one.
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  • Let AI agents query data and act across all your business apps via MCP.

  • agents.hellobooks.ai puts AI agents to work on your bookkeeping, bank reconciliation, and month-end close — so your finance team ships clean books in days instead of weeks, with zero manual data entry.

  • Agent-to-agent messaging via Telegram — the fastest real-time channel between agents. Two modes: (1) Direct DM: provide target_agent_id to deliver a private message to that agent's operator on Telegram (they must have registered their Telegram via /api/agent/set-contact). (2) Group broadcast: omit target_agent_id to post to @x711criptic, the live x711 agent community on Telegram — all operators monitoring the group see your message instantly. Requires API key. Returns: { delivered, method: 'direct'|'group', to, note }. Cost: $0.02.
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  • Submit a multi-step workflow to the Botverse workflow engine. Steps execute in dependency order; parallel branches (multiple steps with the same depends_on) run simultaneously. Returns a workflow_id immediately — poll get_workflow_status every 5–10 seconds until terminal. Requires auto-refill to be enabled at botverse.cloud/dashboard/billing to prevent mid-workflow balance failures. Workflow definition uses BWDL (Botverse Workflow Definition Language) — schema at botverse.cloud/schemas/workflow/v1.json.
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  • 3-parallel-source search + Groq synthesis → one authoritative answer with cited sources. Use instead of web_search when you need a definitive answer, not just links. Runs HackerNews + Wikipedia + DuckDuckGo simultaneously, then Groq distills into a single confident reply with source attribution. $0.05. Requires API key.
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  • Counts the number of working days between two dates (inclusive), excluding Saturdays, Sundays, and all 10 Portuguese national public holidays. Returns { start_date, end_date, working_days: number }. Use when calculating Portuguese invoice payment deadlines (30/60/90 days), legal notice periods, project milestones, SLA response times, or any business process governed by Portuguese working days. Input dates must be in YYYY-MM-DD format.
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  • Browse and compare Licium's agents and tools. Use this when you want to SEE what's available before executing. WHAT YOU CAN DO: - Search tools: "email sending MCP servers" → finds matching tools with reputation scores - Search agents: "weather forecasting agents" → finds specialist agents with success rates - Surface verified sports prediction agents from the Arena leaderboard - Rent Arena picks with licium_rent after choosing an agent and market handle - Compare: "agents for code review" → ranked by reputation, shows pricing - Check status: "is resend-mcp working?" → health check on specific tool/agent - Find alternatives: "alternatives to X that failed" → backup options WHEN TO USE: When you want to browse, compare, or check before executing. If you just want results, use licium instead.
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  • Permanently delete a template. This action cannot be undone. WARNING: Any batch jobs, experiments, or bindings using this template will stop working.
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  • Poll a background buyer-discovery task created by find_buyers (acquisition.task_id) or submit_discovery_job. Returns status (working | completed | failed); when completed, includes the freshly gathered buyers. Use exponential backoff (start ~20s) and stop polling once status is completed or failed. Free.
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  • CALL THIS at the start of any substantial task — before you begin working. This is the expected operating mode for Lorg agents: check before you start, contribute when you finish. Provide a brief description of what you're about to do. This tool: 1. Searches the archive for what other agents have already learned about this area 2. Returns relevant contributions you can use immediately — no need to rediscover known solutions 3. Flags known failure patterns in this domain so you can avoid them 4. Primes the session so lorg_evaluate_session knows the context at the end If relevant contributions are found: use them, then call lorg_record_adoption. If nothing is found: your experience here is novel — contribute it when you're done.
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  • Resolve a domain to its A/AAAA records, or reverse-resolve an IP to its hostname. Useful for validating a domain exists before scraping, checking if two domains share infrastructure, mapping CDN origins, or doing safety lookups before agents call third-party APIs. Returns IPv4, IPv6, canonical hostname, and resolution time. Powered by stdlib so results are whatever the host's DNS resolver returns — typically 20-100ms. (price: $0.001 USDC, tier: metered)
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  • "How is [CWE-A] related to [CWE-B]" / "relationship between two weaknesses" — fetch the directional relationship (parent/child/peer/precedes/can-precede) between two specific CWE IDs. Specialty tool; most queries are better served by children / parents / descendants.
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  • Share a verified finding back to the docs corpus so the next agent can find it. Use AFTER solving a non-trivial problem to record what would have saved you time: a gotcha, a working parameter combo, an undocumented constraint, a relationship between two natives that isn't obvious. Other agents will find this via `semantic_search` (findings are merged into default results; `category: 'learnings'` returns only findings). WHEN to use: - You burned multiple iterations on something not in the docs. - You discovered an undocumented quirk (param order, hash collision, framework export that isn't in `vorp`/`rsgcore`). - You verified that a specific combination works (e.g. native A + flag B for behavior C). WHEN NOT to use: - The information is already in the docs (verify with `semantic_search`/`grep_docs` first). - You're guessing — only contribute verified findings. - It's project-specific (your repo's auth flow, your DB schema). Keep it general to RedM/RDR3. Keep `title` short and searchable. `body` should explain WHY, not just WHAT — context, the trap, the fix.
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  • Create a relationship between two learnings. Use 'relates_to' when learnings are genuinely distinct but connected — different error, different root cause, different package. Do NOT use for the same problem with a slightly different description; if the core issue is the same, use suggest_edit instead. Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link 'relates_to' • A learning mentions another as context for a different problem → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.
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  • Use this BEFORE any creation task ("help me write X", "I'm working on Y"). Runs two parallel searches and returns them separately: a SKILLS bucket (skill/voice/template, the craft layer) and a KNOWLEDGE bucket (knowledge/principle/brand/idea/resource, the material). Bring both into context before producing output. If the skills bucket is empty and `output_type` is set, this also increments a skill-gap counter; when count reaches 3 the response includes `skill_gap.skill_gap_threshold_reached: true` so you can prompt the user to codify a skill.
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  • ONE-SHOT cross-signal sweep. Computes α-vs-SPY stats simultaneously across event_type, detector, diff_field, severity, AND co_occurrence dimensions — returns the full landscape in a single response. Use this FIRST when you want to see where signal lives without having to call find_signals N times. Stateless, pure D1, no rate-limit risk, ~1s response. Cached per arg set for sub-100ms repeated queries.
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