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440,149 tools. Updated 2026-08-10 23:06

"How to build your own knowledge base" matching MCP tools:

  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Your own hive contribution score for a monthly epoch (free read). Wallet-scoped -- returns ONLY your wallet's own row, never another wallet's score and never a fleet ranking. ``epoch`` is "YYYY-MM" and defaults to the most recently completed month. Contribution quality = marginal_uplift (how much your journaled decisions tightened the fleet's collective estimates) x outcome_quality (how well your stated expectations matched on-chain-verified outcomes) x novelty (how much your contributions differ from everything else in the pool -- copies score near zero). ``flags`` explains a zero score, e.g. below the activation threshold or no on-chain-verified outcome. Any recognition of contribution quality from the Community Rewards Pool is discretionary, retroactive and effort-gated -- never a yield, a return, an earn-rate, or a promise of a future allocation. Workflow: REVIEW step -- read alongside get_my_performance to see how your journalling habits, not just your P&L, land with the network.
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  • Built-in product help — ask a natural-language "how do I…" question about Fastio and get a grounded, product-aware answer (or a short clarifying question) back in one call. EXPLAIN-ONLY / ADVISORY: it returns GUIDANCE TEXT and performs NO platform action (it will not create shares, move files, or change anything) — read the guidance, then act with the other tools. Answers are grounded in Fastio's own how-to knowledge AND phrased in terms of these MCP tools — they name the concrete `<tool> action="…"` calls to make — so prefer this over guessing endpoints or burning exploratory calls. For Q&A over YOUR uploaded files (RAG) use the `ai` tool instead — `how-to` answers questions about Fastio ITSELF. FREE and requires only an authenticated user (no org, no plan gate, no billing). Call action='describe' for the full action/param reference.
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  • Read-only full-text search over this tenant’s PUBLISHED knowledge-base articles (playbooks, policies, how-tos); unpublished drafts are never returned and the tenant is fixed by your credentials. Reach for this FIRST to ground an answer in official, tenant-specific guidance before replying to a customer or drafting a resolution. Returns articles ranked by relevance, each with its id, title, a highlighted snippet, and updatedAt: search uses AND semantics, so every word in the query must match. [free]
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  • Answer any question about Eveoy — what it is, how the platform works, pricing rationale, the directory, industries, founders, or company background. Backed by Eveoy's live knowledge base. Use this when the user wants to: - Understand what Eveoy is or does - Learn how the verified-visit / $24.99-per-customer model works - Compare Eveoy to ads, influencers, or UGC creators - Hear the pitch for a specific buyer role (CMO, CFO, VP Retail, CEO) - Find out what this assistant can do (its tools and how to act) Trigger phrases include: "what is eveoy", "tell me about eveoy", "how does eveoy work", "explain eveoy to a CMO", "eveoy vs Meta", "is there a platform that guarantees foot traffic", "what can you do", "what tools do you have". Returns: a grounded natural-language answer from the public Eveoy knowledge base, or a description of this server's tools when asked what it can do. Do NOT use this for: an exact price (use get_pricing), the industry list (use list_industries), directory search (use search_directory), or booking (use start_checkout / book_demo). Cost: free. Latency: 1–3s. Read-only.
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  • Add policy/reference text to a domain's knowledge base. The text is chunked and embedded; explanations for future decisions in this domain will cite it. Creating a new domain claims it for your account (plan limits apply). The built-in demo domains are read-only — ingest into your own domain instead. On team plans, only the domain admin (the member who created the domain, or the subscription owner) can add documents. Args: domain: Domain to ingest into (existing or new). text: The policy or reference text. source: Optional source name shown in the document list.
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  • AI-powered knowledge base for Double - Thank You with semantic search and question answering.

  • Search verified local businesses, check what their verification proves, and message them.

  • List merchant knowledge base documents (uploads + scraped URLs). Use reviewStatus/syncable to see what is ready for agent retrieval. Pass `updatedAfter` for delta sync. Reviewed content is fetched via GET /v6/merchant/ai/knowledge/{id}/content; source audit text is available with ?variant=extracted.
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  • Agent Brain — Reason over a question or task with your agent's own persistent memory in the loop: recalls up to 12 relevant memories from your agent's private scope, reasons with Claude, and writes up to 3 new memories back, so the agent improves with every call. Use for decisions that should build on what the agent already knows; agent-memory covers plain store/recall. Input: {think: string}. Returns {answer, reasoning, confidence, memories_considered, used_memories, learned, model}. (8 MESH/call, a tool · cognition)
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  • Search the Klever VM knowledge base for smart contract development context. Returns structured JSON with matching entries, scores, and pagination. Use this for precise filtering by type or tags; use search_documentation for human-readable "how do I..." answers.
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  • Hand a natural-language prompt to the FreeAppStore VibeCode AGENT — the platform's own AI writes the code AND deploys it. This is different from create_app/update_files (where the CALLING model writes the code): here you just prompt, and the platform builds. Uses your stored AI key (provider must be in your vault). Long-running; it builds in the background. Returns the session_id — poll agent_status to watch it and get the live URL. Tip: include the app id in your prompt, e.g. 'Build a dice roller and deploy it as dice-roller'.
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  • Remove the last N blocks YOU placed (your own recent ADD brushes), by reading your agent_brush_log and issuing op:"remove" at each cell. Default n=1, max 20. Only reverses ADDs (a prior remove is skipped). Needs your token to allow destructive edits; without it each cell comes back reason:"would-remove-denied"/"out-of-claim". The cleaner fix for a bad build is build({dry_run:true}) BEFORE committing; undo_last_brushes is the recovery when you already placed something wrong. Returns { undone, failed, failed_cells }.
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  • Build a NON-CUSTODIAL EVM DEX swap via KyberSwap aggregator on eth/base/bsc/arbitrum/polygon/optimism/avax: returns UNSIGNED calldata {to,data,value} — sign with YOUR OWN wallet. Server never touches funds. 0.3% routing fee. ERC20 input needs prior approval.
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  • Search across the nTop knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about nTop, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages. If you need the full content of a specific page, use the query_docs_filesystem tool to `head` or `cat` the page path (append `.mdx` to the path returned from search — e.g. `head -200 /api-reference/create-customer.mdx`).
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  • Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.
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  • Generate a personalized Canton Network developer onboarding/quickstart path. Use when a developer asks how to start building, build a dApp, or develop on Canton specifically. Canton-only. Do not use for onboarding to other chains or tools. Ask the user's background first (EVM, Solana, Sui/Move, Web, Enterprise, or New to Blockchain). Prefer this over 'search' for 'how to build / get started on Canton'; use get_faq for a single specific gotcha and get_api_reference for API details.
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  • Explains how the payment rail works for agents: paying from your own wallet with no account, wallets we manage, spend mandates, reputation, receipts and intent enforcement. Call this first if you are new to the wallet_* and data_* tools.
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  • Add backlink order(s) to your basket (no charge yet). Pick ONE content mode: (auto) send donor_ids + target_url + keyword and we write the article; (prompt) also send "recommendations" to steer the AI writer — fetch the editable base prompt with get_article_prompt first; (own) send target_url + "content" (+ optional "title","article_url") to publish your own ready HTML (requires own-content enabled). Then call publish_basket to pay from balance.
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  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Look up what an integrator's own-fee routing earned, by appCode (the identifier you tag into appData: your widget appCode or your SDK ophisReferrer code). Returns routed volume (USD, split by chain and by sovereign-vs-hosted), the Ophis base fee charged on your flow, your OWN stacked fee, and your referral rebate paid-to-date with payout tx links. Guaranteed/paid figures are scoped to the Ophis-operated chains (Optimism, Unichain); CoW-hosted figures are accrued at settlement and disbursed by CoW under CoW terms (see the response `disclaimer`). Read-only, keyless, cumulative (no current-cycle or next-payout data).
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  • Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as `query`. If the first results miss, rephrase once and retry.
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