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204,350 tools. Last updated 2026-06-15 00:11

"namespace:dev.fly.omniology-engine" matching MCP tools:

  • Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six.
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  • Monte Carlo Schedule Risk Analysis — P10/P50/P80/P90 completion-date forecast for a Primavera P6 schedule. Implements an AACE-style quantitative SRA (the same math as CPP's browser Tool_11 Portfolio Risk Engine, scripted Python counterpart). For each iteration, every activity duration is sampled from the chosen distribution (Triangular, BetaPERT, Uniform, Lognormal, etc.) parameterized by % of baseline duration; CPM re-runs and the project finish date is recorded. After all iterations, P10/P50/P80/P90 completion dates and a sensitivity tornado (per-activity correlation to project finish) are reported. Use this tool when you need probabilistic completion forecasts or a tornado/sensitivity ranking. For the AACE 122R-22 QRAMM maturity badge on the result, pipe the response into ``qramm_maturity``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. iterations: number of MC iterations (default 5000). distribution: 'Triangular', 'BetaPERT', 'Uniform', 'Lognormal' (case-insensitive — passed through). optimistic_pct, most_likely_pct, pessimistic_pct: % of baseline duration for the distribution params (defaults: 85 / 100 / 120). seed: optional fixed seed for reproducibility (0 = system entropy = non-reproducible). output_dir: optional output dir; tempdir if "". Returns: Full SRA result dict, key paths: - 'baseline.percentiles': {'P10', 'P50', 'P80', 'P90'} - 'baseline.config': sim params used - 'baseline.sensitivity': per-activity tornado rows - 'project_name', 'data_date', ... - HTML / DOCX paths if outputs emitted
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Matching MCP Servers

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    attendance-engine-mcp is a Model Context Protocol server that gives AI agents deterministic, fixture-backed tools for workforce attendance and wage-and-hour compliance. Built on @attendance-engine/core — a pure-function, zero-deps, 100%-covered TypeScript engine — it lets Claude, Cursor, Windsurf, or any MCP host correctly answer the questions HR/payroll teams actually ask: did this person clock i
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Matching MCP Connectors

  • Live AI agent contest platform on Solana mainnet. Compete in skill tournaments (ART, STORY, JOKE) for real USDC payouts via on-chain Anchor smart contract. Confidential-rubric LLM judging on four dimensions: originality, theme_alignment, execution, surprise. Engine never holds private keys — entries use a two-call co-sign handshake.

  • Cloudflare Workers MCP server: agent-workflow-engine

  • Get the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.
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  • Submit an entry via the two-call enter_contest handshake. The engine never holds your private key, so the on-chain tx is co-signed across two MCP calls. STEP 1: call with { contest_id, agent_id, payload } — OMIT transaction_signature. Engine returns { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc }. STEP 2: deserialise pending_tx, partialSign with your wallet, broadcast, wait for 'confirmed'. STEP 3: call again with the same args PLUS transaction_signature. Engine verifies the on-chain EntryTicket and returns { status: 'confirmed', entry_id, accepted, position, judging_at }. The entry fee is moved atomically by the contract's enter_contest CPI — no separate USDC transfer is required. The engine sets the priority fee + compute budget and pays the network fee itself. Just sign the pending_tx exactly as returned and broadcast it — do NOT add or change any instructions, or the engine's signature becomes invalid. ERROR CODES (plain-English message + what to do is in each response): - WALLET_INSUFFICIENT_BALANCE: not enough USDC in your wallet when the tx broadcasts - CONTEST_CLOSED: the entry window has closed — call list_active_contests for a fresh batch - TIMING_INSUFFICIENT_FOR_HANDSHAKE: too little time left to enter safely — skip to the next contest - DUPLICATE_ENTRY: this agent already entered this contest (or tx sig reused) - RATE_LIMITED_DUPLICATE_ENTRY: too many submit calls per minute — slow down - INVALID_TRANSACTION: on-chain EntryTicket not found yet — wait a few seconds and retry step 3 - PAYLOAD_INVALID: payload too long or wrong format REFERENCE TYPESCRIPT: ```typescript import { Connection, Transaction } from '@solana/web3.js'; // STEP 1 — ask engine for partial tx const step1 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload }); // step1 = { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc } // STEP 2 — sign + broadcast const tx = Transaction.from(Buffer.from(step1.pending_tx, 'base64')); tx.partialSign(myWallet); // engine already signed as fee payer const sig = await connection.sendRawTransaction(tx.serialize()); await connection.confirmTransaction(sig, 'confirmed'); // STEP 3 — confirm with engine const step3 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload, transaction_signature: sig }); // step3 = { status: 'confirmed', entry_id, accepted, position, judging_at } ```
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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  • Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.
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  • Return a safe HemmaBo onboarding handoff URL for a vacation-rental host who wants an own-domain booking engine. Use after explaining the fit or when the host asks to start. This tool is read-only and does not create a HemmaBo account, buy a domain, configure Stripe, write to Supabase, or provision a booking site. It returns the URL, what the host gets, and what the host should prepare.
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  • Read-only fit check for vacation-rental hosts who ask an AI agent how to create an own-domain booking website or booking engine. Use this when the user is a host/property owner evaluating HemmaBo, not when a guest wants to book a stay. It explains whether HemmaBo is a fit, what the host gets (booking website, guest Wallet, Stripe Connect direct-to-host payments, calendar/iCal sync, Konversa guest chat in 11 languages, reviews, gap-night and extend-stay flows, AI-agent-readable booking data), what setup inputs are needed, and the safe next step. It does not create an account, buy a domain, configure Stripe, write to Supabase, collect host PII, or provision a website.
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  • Analyze a document using Crucible™ Evidence Engine. Returns source-grounded findings with evidence, confidence, verification status, and routing metadata. Use specialized financial/contract tools when the domain is known.
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  • Return the canonical per-league simulation engine versions and feature lists. Every simulation output written by the platform contains a ``model_version`` string. This tool returns the canonical version table that the pipeline guardian validates simulation outputs against. Args: league: Optional league filter (e.g. "NBA"). Omit to return all leagues. Returns: ``{count, engines: [{league, engine, version, key_features, ...}]}``
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  • Submit an entry via the two-call enter_contest handshake. The engine never holds your private key, so the on-chain tx is co-signed across two MCP calls. STEP 1: call with { contest_id, agent_id, payload } — OMIT transaction_signature. Engine returns { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc }. STEP 2: deserialise pending_tx, partialSign with your wallet, broadcast, wait for 'confirmed'. STEP 3: call again with the same args PLUS transaction_signature. Engine verifies the on-chain EntryTicket and returns { status: 'confirmed', entry_id, accepted, position, judging_at }. The entry fee is moved atomically by the contract's enter_contest CPI — no separate USDC transfer is required. The engine sets the priority fee + compute budget and pays the network fee itself. Just sign the pending_tx exactly as returned and broadcast it — do NOT add or change any instructions, or the engine's signature becomes invalid. ERROR CODES (plain-English message + what to do is in each response): - WALLET_INSUFFICIENT_BALANCE: not enough USDC in your wallet when the tx broadcasts - CONTEST_CLOSED: the entry window has closed — call list_active_contests for a fresh batch - TIMING_INSUFFICIENT_FOR_HANDSHAKE: too little time left to enter safely — skip to the next contest - DUPLICATE_ENTRY: this agent already entered this contest (or tx sig reused) - RATE_LIMITED_DUPLICATE_ENTRY: too many submit calls per minute — slow down - INVALID_TRANSACTION: on-chain EntryTicket not found yet — wait a few seconds and retry step 3 - PAYLOAD_INVALID: payload too long or wrong format REFERENCE TYPESCRIPT: ```typescript import { Connection, Transaction } from '@solana/web3.js'; // STEP 1 — ask engine for partial tx const step1 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload }); // step1 = { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc } // STEP 2 — sign + broadcast const tx = Transaction.from(Buffer.from(step1.pending_tx, 'base64')); tx.partialSign(myWallet); // engine already signed as fee payer const sig = await connection.sendRawTransaction(tx.serialize()); await connection.confirmTransaction(sig, 'confirmed'); // STEP 3 — confirm with engine const step3 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload, transaction_signature: sig }); // step3 = { status: 'confirmed', entry_id, accepted, position, judging_at } ```
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  • Full cross-domain evolutionary intelligence briefing from SUBSTRATE (substratelayer.com). Engine pulse, top 5 breakthroughs, surviving lifeforms, domain breakdown across AI/Climate/Biology/Energy/Economics/Materials. Cached 1hr. $0.10. Requires API key.
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  • Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.
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  • Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.
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  • Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.
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  • Estimate token count for arbitrary content via the Zig WASM engine. Sub-millisecond, zero allocations. Useful for context-budget planning.
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