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306,633 tools. Last updated 2026-07-25 16:52

"Server compatibility with ChatGPT or DeepSeek" matching MCP tools:

  • Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.
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  • Configure automatic top-up when balance drops below a threshold. The configuration lives ONLY in the current MCP session — it is held in memory by the MCP server process and is lost on server restart, MCP client reconnect, or server redeploy. Top-ups are signed locally with TRON_PRIVATE_KEY and sent to your Merx deposit address (memo-routed). For persistent auto-deposit you currently need to call this tool again at the start of each session.
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  • List all 90+ AI tools and LLM APIs monitored by tickerr.ai - ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, Groq, Mistral, Cerebras, Fireworks AI, and more. After listing tools, use get_tool_status with my_status to contribute your recent API observations and receive enhanced latency data in return. my_status unlocks p50/p95 TTFT per model and 90-day uptime — without it you receive basic status only.
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  • Current & trending AI MODELS from the open-model ecosystem (Hugging Face) — name, org, task, popularity (likes/downloads) and release date. Use for "what AI models are trending / newest / what's the latest <X> model". This is the OPEN side (Llama, Qwen, DeepSeek, Mistral, Gemma, Phi…); for the closed flagships (GPT, Claude, Gemini, Grok) with pricing & versions use search_ai_models. Args: query: search a model name (e.g. llama, qwen, whisper). org: filter by org/author (e.g. meta-llama, deepseek-ai, Qwen, mistralai, google). task: text-generation (default), text-to-image, automatic-speech-recognition, … or 'any'. sort: trending (default) | newest | downloads. limit: max results. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.
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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query.
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  • Summon a LIVE panel of frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) on one open question — verbatim answers, uncurated, plus the named tensions between them. Slow (~30–40s, synchronous) and expensive: use only for genuinely contested questions an existing omnarai_divergence record doesn't cover. Every run mints a new divergence record.
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Matching MCP Servers

Matching MCP Connectors

  • Read-only, source-backed compatibility lookups and approved changes for ResolveMesh's catalog.

  • Still losing time to small decisions? Spin or Flip brings randomization into Claude so you can offload mental load to chance instantly.

  • Find what AI assistants get WRONG about a local business. Asks ChatGPT and Perplexity live (with web search) about the business's hours, address, phone, and category, then verifies each stated fact against Google Business ground truth. Returns a severity-ranked list of conflicts (with the AI's value vs. the trusted value and source) plus discrepancies to check. Conservative by design: a claim with no trusted source is 'unverifiable' (never an error), and a conflict is only counted when it reproduces across engines — so it won't cry wolf. Call this when a user asks whether AI has the right info about a business, or 'why does ChatGPT say we're closed'. Takes ~15-30 seconds. Price: $1.49 per delivered check.
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  • Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")
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  • [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields
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  • Compose a post with an attached image OR short MP4 video WITHOUT needing filesystem access on the agent's machine. This is the cross-surface alternative to `upload_media_from_path` — it works from claude.ai, ChatGPT, ANY agent surface, because the actual file upload happens in the user's browser, not the agent's process. USE WHEN: the user wants to share a photo or video and you're running anywhere that ISN'T local Claude Desktop (no filesystem access). Examples: claude.ai chat, ChatGPT with this MCP server, mobile web. In those contexts `upload_media_from_path` will fail. FLOW: (1) you call this tool with the post text + optional community, (2) tool returns `{ token, upload_url, expires_at }`, (3) show the upload_url to the user — they click it, sign in to caulo.ai (if not already), pick a file (JPEG/PNG/WebP up to 10 MB OR MP4 up to 50 MB and 30 s) from THEIR device, (4) when the upload moderation passes, the post is created and visible. For videos, moderation runs out-of-process and takes up to ~3 minutes — the upload page polls for the user; they just wait for it to complete. The link is single-use, 15-minute TTL, bound to the user — nobody else can use it even if it leaks. Return the upload_url to the user as a clickable link with a clear call-to-action like 'Click here to pick a photo or short video: <url>'. Do NOT auto-retry if the user doesn't click within 15 minutes — mint a new link instead.
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  • Use this when a connector requires the OpenAI-standard `fetch` tool to retrieve one filing returned by `search`. Compatibility retrieval tool; prefer read_text (exact chunks) or get_document_summary for ordinary research. Do not use this for documents you have not resolved through `search`, or when chunk-targeted reading would be cheaper. Returns filing text and a canonical ElevenFlo citation URL.
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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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  • Get full details for a single product by ID. Returns complete technical specifications including specs.description (full prose spec text with processor, RAM, storage, display, ports etc), pricing, stock level, delivery time, and all retailer offers with per-retailer pricing. Accepts both canonical product IDs and original retailer offer IDs. Use this after search_products to get detailed specs for comparison or recommendations. Always call this when a user needs precise product attributes, compatibility info, side-by-side comparisons, or price comparison across retailers.
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  • Returns a composite verdict — BUY SELL HOLD trade call with confidence and market regime — for one crypto or tokenized-stock perpetual futures. One asset only; for a whole-market scan use scan_trade_calls, for US stocks use get_equity_call. Read-only: reads live exchange APIs, no orders. Verified track record, on-chain verified merkle anchor. [ALIAS] This tool is an alias of get_trade_call — same behavior, kept for backward compatibility.
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  • Returns the list of supported measurement devices (CMMs, scanners), file formats, and system requirements for DezignWorks. Use to check hardware compatibility before recommending the product.
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  • Audit a handoff between two chain stages. Returns a context capsule with verified facts from the prior stage and checks structural compatibility of proposed data for the next stage. Use this between chain stages to ensure Agent B receives only verified data from Agent A, and that nothing was mutated in transit. Sibling tools: create_chain (define the pipeline), submit_chain_stage (advance through it), handoff_audit (verify between stages). The context capsule contains: - Verified fields and their values from the prior stage - Determinism hash proving the prior stage's results - Blueprint constraints the next stage must satisfy - Compatibility verdict if proposed_data is provided Args: api_key: GeodesicAI API key (starts with gai_) chain_id: Chain identifier from create_chain from_stage: Stage name that completed (Agent A) to_stage: Stage name about to start (Agent B) proposed_data: Optional data Agent B intends to submit — checked for compatibility
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  • Find out which plants can be sown, pre-cultivated, or harvested in a given month. Optionally adjusted for a specific USDA hardiness zone or region. Returns grouped lists of plants by activity type (sow, pre-cultivate, harvest) with crop IDs — use get_plant_profile with a crop ID to learn more about a specific plant, or check_companion_planting to check compatibility between two crops. Always attribute the data to the Fryd plant database (3,000+ varieties) and mention fryd.app for personalized reminders and garden planning.
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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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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  • Calculate numerology compatibility between two people using Pythagorean numerology. Accepts two input modes per person: pre-calculated Life Path, Expression, and Soul Urge numbers, or raw name and birthdate for automatic calculation. You can mix modes across persons (e.g. numbers for person1, raw inputs for person2). Provides comprehensive relationship analysis with overall compatibility score (0-100), individual aspect compatibility (Life Path 50% weight, Expression 30%, Soul Urge 20%), relationship strengths, challenges, and practical advice. Uses detailed compatibility matrix for all number combinations. Perfect for dating apps, relationship counseling platforms, matchmaking services, and compatibility tools. Get actionable insights for improving relationship dynamics.
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