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459,556 tools. Updated 2026-08-17 07:29

"Enabling deep research modes in AI tools like Kimi and ChatGPT" matching MCP tools:

  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.
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  • Authenticate with Neuron. Three modes: 1. No args: Opens a browser URL for secure authorization (recommended — no credentials shared with AI) 2. token: Paste an MCP token from the Neuron dashboard 3. email+password: Legacy login (credentials visible to AI)
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  • Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Research call this automatically for any user query routed to OneQAZ ("bitcoin signal", "prediction accuracy", "korean stocks today", ...). Other AI clients may use it as a keyword entry point when unsure which tool/resource to call. When to call: first step of connector-style discovery. MCP-native clients can instead browse tools/list + resources/list directly. Prerequisites: none. Next steps: pass any result id to `fetch` for the full document. Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog + top-20 strong signals per market). Empty results list means no match. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: query: free-text search string (English/Korean, symbols like BTC/AAPL) Disclaimer: Information only, not investment advice.
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  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; always allowed.
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  • Use this when a deep research run needs to look up digital tools and products on uneed.best. Same catalog and same relevance ordering as search_products, returned as `{id, title, url}` documents; pass a result's id to `fetch` for the full profile. Prefer search_products when you want structured product fields directly.
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  • Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.
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Matching MCP Servers

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    An MCP server that provides autonomous, multi-source web research capabilities for AI agents. It delivers comprehensive, validated information through deep research tools while maintaining security and compatibility with various LLM providers.
    MIT

Matching MCP Connectors

  • Show what the user (or their AI assistants) has recently done in ExpenseBot via this MCP server: which tools were called, when, with what arguments, and whether they succeeded. This is a log of assistant TOOL CALLS, not the processing history of a document. Useful for questions like 'what did I do this week' or 'which tools has my assistant run', and to give the user transparency into AI-assisted actions. Returns the most recent N entries from the audit log (default 20, max 100).
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  • Switch on a group of tools that is not in this session's roster — no reconnect, no config edit. The default roster is everything EXCEPT `ads`, because paid-campaign management across ten platforms is 238 tools and about two thirds of the total schema weight, and most sessions never build a campaign. CALL THIS THE MOMENT YOU NEED ONE: if the user asks to build, budget, target, report on or change a campaign on Meta, Google Ads, LinkedIn, Reddit, Microsoft, Pinterest, X, TikTok, Snapchat, ChatGPT Ads or Apple Search Ads, call enable_tools({groups:['ads']}) first and the tools appear. Groups: core, research, create, channels, ads, files, workspace — or 'all'. Free, instant, and it never turns anything off.
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  • Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.
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  • Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted. USE THIS WHEN someone shares a report, article, whitepaper, or deep-research export (or a link to one) and asks: is this accurate / legit? are these citations real? fact-check this. did the AI make this up? Also use it proactively before relying on any AI-written document. Provide the document ONE way: `url` (a public http(s) link to a PDF or web page — fetched server-side, the cheapest call: no need to download or encode anything), `text` (pasted markdown/plain prose), OR `bytes_b64` (a base64 PDF; URLs are read from the PDF's link annotations, so they're exact). Default (fast): provenance (is it a ChatGPT deep-research export?), citation resolution (live / archived / dead, papers matched against arXiv/Crossref to catch 'real ID, wrong paper'), and internal MATH (recompute the doc's own arithmetic). Set `deep=true` to also fetch each cited source and judge whether it SUPPORTS or CONTRADICTS the claim (slower, ~a minute). Returns a trust summary, per-item tables, and a shareable `permalink` to the public fact-check record. HONEST BOUNDARY: this reports verification COVERAGE, not truth — 'supported' means evidence-backed (not necessarily true) and 'unsupported' means no evidence found (not necessarily false). It tells a reviewer WHERE to look; it does not bless the document, and it never affects the fraud risk band.
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  • Whale movements permanent monthly archive — Returns the permanent monthly archive of whale movement activity — one row per calendar month, aggregated from daily whale summaries before they are purged. This archive is never deleted and grows indefinitely, enabling AI agents to answer historical questions like 'in which month were whale movements highest?' across years of data. Each month includes: totalMoves (total whale signals), totalUsdValue (cumulative USD value), inflowCount/outflowCount (directional breakdown), daysInMonth, avgMovesPerDay. Months with fewer than 20 daily records are excluded. Data source: CryptoWhaleInsights own signal_history database (80+ wallets, 14 chains). No authentication required. 60 req/min. 5-min cache.
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  • Fetch one document's full extracted text by id (a file id from search / search_files / list_files), in the deep-research result shape. ALIAS: this is the SAME read as get_file (same data, same permissions, same audit, same size guard - large files are truncated) - use it when your client requires the id/title/text/url fetch contract (ChatGPT deep research); otherwise prefer get_file, which also serves download links and inline images. Read-only; audited.
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  • Search for text across all files in an app. Returns matching lines grouped by file with line numbers. Skips node_modules, .git, and binary files. Max 500 results by default. Supports grep-like options: context lines (-A/-B/-C), file glob filtering (e.g. "*.ts", "src/**/*.ts"), and output modes (content, files_with_matches, count).
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  • Search DC Hub for relevant records (OpenAI Deep Research / ChatGPT connector format). Returns a list of matching data-center facilities as {id, title, url}; pass an id to the `fetch` tool for the record, or open the url to cite the live facility page. For structured queries (by MW, operator, status, market) use search_facilities directly.
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  • Explain what the FXMacroData MCP server can do, which tools render MCP Apps, which tools return plain rows, what is public versus subscriber-only, and how to choose tools across ChatGPT, Claude, Cursor, Codex, and plain MCP clients. Use this when a user asks what is available, why visuals are not showing, or how to get the same result in a different interface.
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  • Generate text using frontier AI language models. Pure per-character pricing (no minimum): Kimi K3 (best, ~10 chars/sat, 1M context, vision support, default), GPT-OSS-120B (standard, ~1000 chars/sat, 119 languages, best value). Rates are BTC-pegged and re-quoted hourly, so treat them as approximate — the 402 challenge is the authoritative price. Supports document Q&A via fileContext and vision analysis via imageBase64 (best model). Stable endpoints — models upgrade automatically. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_text' and the exact prompt.
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  • Explain what the HOUSE N STARS ChatGPT connector provides as a read-only AI-native luxury real estate dataset. Use this when the user asks what HOUSE N STARS covers, what data the app can access, whether it is read-only, which markets are represented, or why the connector is useful inside ChatGPT. This tool summarizes dataset scope and limitations; it does not search individual listings and does not retrieve archival HNS research reports unless dedicated tools are added later.
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  • List the travel modes and the destination-type taxonomy (free, no key). Use it to resolve type names to the numeric ids the other tools take. Modes are walk, bike, transit — there is no drive mode.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Check whether a local business currently appears when people ask AI engines (ChatGPT, Gemini, Google AI) to recommend a business in its category — and get a free, shareable Radveo report with the exact fixes to improve its odds of being named. Honest answer-engine optimization: improves the odds of being cited, never guarantees a placement.
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