OpenAI ChatGPT Deep Research / Connectors search contract. Returns matching Dynamoi artists, campaigns, and Smart Links so they can be cited in a deep-research session. For regular ChatGPT chat use dynamoi_search instead.
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737,172 tools. Updated 2026-11-02 19:28
"Enabling deep research modes in AI tools like Kimi and ChatGPT" matching MCP tools:
- List all agents in this workspace with their status, project, last-run time, and result count. Use to see which other AI tools (ChatGPT, Gemini, Kimi, Manus, other Claude sessions) have been active and what they have been working on.ConnectorNo auth
- 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; nothing is written, so it is safe to call.ConnectorNo auth
- 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. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.ConnectorNo auth
- 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.ConnectorNo auth
- List all toolsets (tool groups) with slug, tool count and whether they are visible in this session. Core toolsets are always visible; enable others with enable_toolset. Full-access API keys see every tool; scoped keys and OAuth sessions automatically see every tool their scopes or role allow. Hidden tools stay callable — enabling only affects what tools/list advertises.ConnectorAPI key
Matching MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables deep research tasks using a multi-agent architecture that integrates any LLM and MCP tools. Available via MCP stdio, streamable HTTP, and SSE transports.16MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to perform deep web research and generate comprehensive reports using a multi-agent divide and conquer approach.1-
Matching MCP Connectors
- Fodda Deep ResearchOAuth
Autonomous deep research reports merging PSFK trend graphs with citable sources.
Read-only MCP over the LivingMeta AI-in-Research corpus: 12,400 papers, gaps, priority agenda.
- What AI engines answer for a prompt, with the brands they mention and the sources they cite. Slow: returns a task_id, then read the result with get_task. Use when: "does ChatGPT recommend us for X", "who do AI engines cite for X", an AI visibility baseline for a brand across engines. Not for: which prompts people ask, use aeo_search_prompts. Google's ten blue links, use seo_get_serp (ai_overview: true adds the overview to it at 1 credit). Whether a site is ready for AI crawlers, use aeo_get_site_readiness. Returns: Task. task_id now; get_task returns rows[] per prompt and engine (answer, mentions[] (brand, mentioned, position, cited), citations[] (position, url, domain, title), search_results[] (pages retrieved, cited or not), search_queries[] (the web searches the engine ran), named_brands[] (brands ChatGPT tagged in the answer, not only yours)) and errors[] for cells that failed. answer null = the engine showed no AI answer. search_results[] and named_brands[] are filled for chatgpt only, search_queries[] for chatgpt and claude. named_brands[] drops a tag ChatGPT gives as a description instead of the brand, so it can miss brands; read the answer for a full list. chatgpt and gemini are what their apps show a person, model chosen by the app; claude and perplexity come from their model APIs. Answers are live and non-deterministic; results are kept 30 days and never cached. Cost: per prompt, 15 credits for claude, 6 for perplexity, 2 each for chatgpt, gemini, ai_overview and ai_mode. Default 1 prompt on the five default engines = 27 credits. A cell that fails is not charged. Example: {"prompts": ["best crm for startups"], "brands": ["HubSpot", "pipedrive.com"]}ConnectorAPI key
- Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest score-ranked 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 starting 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 signals by |score| per market). Ticker / company-name queries (BTC, NVDA, 삼성전자) resolve to `signal:{market}:{symbol}` first; market words (S&P, KOSPI, crypto) surface that market's resources. Every result id is fetchable. Empty results come with a `note` explaining coverage. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Output nature: Observed signal state computed from live market prices each cycle. signal_score is an internal ranking score, not a probability; bias is its sign. Context for the user's question, not investment advice.ConnectorNo auth
- For research and citation lookups (deep research): free-text search of live Northern Cyprus (KKTC/TRNC) property listings on Evlek, in Turkish, English, Russian, German or Arabic. Returns matching listings as id/title/url, newest first (sale and rent as two groups when the query names neither). The answer language follows the language of the query. OpenAI deep-research compatible format, for research and citation clients. Use when: the caller only has a free-text query ("2+1 apartment in Kyrenia under 150000 pounds", "EVL-100464"). Not for: structured filters, paging or choosing the answer language.ConnectorNo auth
- 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.ConnectorNo auth
- 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.ConnectorNo auth
- Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).ConnectorNo auth
- Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).ConnectorNo auth
- Searches the UNESCO UIS statistics (≈5,000 indicators: education — enrolment, completion, literacy, teachers, spending, SDG 4 —, science/R&D (SDG 9.5), culture (SDG 11.4) and demographic context) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `uis_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.ConnectorNo auth
- Searches the Banco Central do Brasil time series (SGS: interest rates, inflation, exchange rates, credit, fiscal and external sector — the curated catalog plus the open data portal index) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `bcb_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.ConnectorNo auth
- Searches the IBGE (Brazilian official statistics: SIDRA tables, municipalities, known indicators) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `ibge_*` tools (`ibge_sidra`, `ibge_cidades`, `ibge_indicadores`, `ibge_comparar`…), which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.ConnectorNo auth
- Searches the medical terminologies (CID-10 categories and chapters, ICD-11, LOINC, RxNorm, MeSH, terminology version records) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the terminology tools (`icd11_*`, `cid10_*`, `loinc_*`, `rxnorm_*`, `mesh_*`, `atc_*`, `map_*`, `find_equivalent`, `validate_codes`), which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.ConnectorNo auth
- Search Silicon Analysts — returns up to 10 matching records as {results: [{id, title, url}]}: curated market datasets and their individual series (HBM contract $/GB by generation, DRAM/NAND pricing, TSMC wafer price history, CoWoS capacity and lead times, capex), the reference pages and tool domains behind them (wafer pricing by node, AI accelerator costs, foundry allocation, HBM market and qualification, fab capacity, packaging costs, forecast track record, what changed), and published analysis articles. Every url is a citable https://siliconanalysts.com page. USE THIS for: connector hosts that only speak search + fetch — ChatGPT deep research and ChatGPT company knowledge — and any agent that needs to discover which record answers a free-text question before reading it with fetch. DO NOT USE for: structured or filtered queries when you can call tools directly — the specialised tools (get_market_dataset, get_wafer_pricing, get_foundry_allocation, get_hbm_market_data, ...) take filters and return typed fields; search returns ids, titles and urls only, never values. Deterministic keyword match over published catalogs (no live data read), identical for every tier. An empty or unmatched query returns {results: []}, not an error. Pass a result's id to fetch.ConnectorNo auth
- Find served datasets by keyword and return each one's id, title and citation URL, in the shape ChatGPT deep research and company knowledge call search with. It runs the same search as search_datasets, so every word in query must match. Pass an id to fetch to read the dataset's licence, attribution and newest version. The same id is the slug that list_fields, query_rows and count_rows take. Up to 50 matches come back in one answer, with no paging. Each call costs one of the 60 queries each address may make in 10 seconds.ConnectorNo auth
- Shows what Proofsource measures, without signing in: a real visibility card for one example brand (Tesla), from answers collected from ChatGPT, Perplexity, Claude and Google AI Overviews. It shows how often AI answers mention the brand, per engine, which competitors they name instead, and sample answers. Use it when someone asks whether their brand shows up in AI answers and has not signed in. It always shows the example brand; to check their own brand they sign in.ConnectorNo auth
- Keyword search over the user's own library: matches transcription titles and transcript text (case-insensitive substring; not semantic). Returns `results[]` of `{id, title, url}` where `id` is the transcription id as a string — pass it to `fetch`, or to the numeric-id tools after `Number(id)`. Transcript-text hits carry a `url` with `?t=<sec>` at the matching segment. This tool + `fetch` implement the ChatGPT connectors / deep-research contract; for structured chapters / sections / Q&A / timestamps prefer `getTranscriptionResult` and `getTranscriptionTimestamps`.ConnectorAPI key