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458,502 tools. Updated 2026-08-17 03:36

"Exploring Deep Search Techniques or Tools" 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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  • Search the MITRE D3FEND catalog of defensive techniques by keyword, tactic, or targeted artifact. Default response is SLIM (drops `uri` from each row — saves ~60 chars/row, ~30% on popular drills); pass include='full' for the verbose record. Pass exclude_id when chaining from d3fend_defense_lookup to skip self in sibling-artifact searches. Use to discover defenses applicable to a given threat model — e.g. 'what defenses harden access tokens?' (tactic=Harden + artifact='Access Token'). Drill into d3fend_defense_lookup with any returned defense_id for the ATT&CK technique mappings. Free: 30/hr, Pro: 500/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.
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  • Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.
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  • Lists every registered jurisdiction with its code, active/inactive status, and supported capabilities — search, entity lookup, quick verification, and deep verification. Free and requires no authentication. Use it to confirm a state or country is supported and which verification tiers it offers before calling verify_business or search_entities.
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  • Search the MITRE ATLAS catalog of AI/ML attack techniques by keyword, tactic, or maturity. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose record. Pass exclude_id when chaining from atlas_technique_lookup to skip self in sibling-tactic searches. Use this to discover techniques matching a threat-model question, e.g. 'what techniques target LLM serving infrastructure?'. Drill into atlas_technique_lookup with any returned technique_id for the full description, ATT&CK bridge, and pivot hints. For broader cross-referencing: when a result has attack_reference_id, that bridges to D3FEND mitigations via d3fend_defense_for_attack. Free: 30/hr, Pro: 500/hr. Returns {query (echoed filters), total, results [{technique_id, name, description (truncated by default), tactics, inherited_tactics, maturity, attack_reference_id, subtechnique_of}], next_calls}.
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  • Load fundamental workflow for valuation, cash flow, margins, balance sheet. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about company valuation, "is X a good buy", financial health, debt levels, profitability ratios, revenue trends, earnings quality, or any deep-dive company analysis. Can be combined with other workflow tools.
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Matching MCP Servers

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    A deep web search MCP server using LinkUp API that provides a deep_search tool for performing deep web searches with optional max results.
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    Enables deep web search across multiple providers including Google, Bing, Brave, DuckDuckGo, and Perplexity, with support for comprehensive AI-powered research using intelligent multi-engine queries.
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Matching MCP Connectors

  • Autonomous deep research reports merging PSFK trend graphs with citable sources.

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

  • 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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  • Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.
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  • Look up a MITRE ATT&CK threat group (intrusion set) or software entry by name or ID for authorized penetration testing and threat intelligence. Returns the group or software record: ATT&CK ID, display name, known aliases, type (group vs. software), description, and the techniques it uses with procedure-level context from public ATT&CK reporting. Accepts exact ATT&CK IDs (G0007 for threat groups, S0002 for software) or keyword/name search (e.g., "APT28", "Mimikatz", "Lazarus Group"). Equally useful for defenders building detection coverage around specific adversary tradecraft.
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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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  • Search patents by keyword across EPO, USPTO, or WIPO. Read-only. No side effects. Idempotent. Returns up to 10 matches. keywords: Search terms describing the invention e.g. neural network image classification. Required. jurisdiction: One of EP, US, or WO. Optional. Default EP. date_from: Earliest filing date in ISO 8601 format e.g. 2020-01-31. Optional, defaults to no lower bound. Returns patent numbers, titles, and filing dates. Use this when finding prior art or exploring a technology landscape without a specific number. Use legal_fetch_patent_by_number instead when you have the patent number already. Verified source: EPO OPS + USPTO. 24-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="legal_search_patents_by_keyword", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".
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  • Search official economic statistics by free text, e.g. 'inflation barbados' or 'government debt japan'. Returns result ids that can be passed to fetch. Designed for deep-research connectors; for richer control use get_indicator / get_series.
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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 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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  • Bulk ATLAS technique lookup — retrieve full records for up to 50 techniques in a single request instead of N separate atlas_technique_lookup calls. Designed as the natural follow-up to atlas_case_study_lookup, whose techniques_used array can be passed directly. Each item is the same shape as atlas_technique_lookup, including parent-tactics inheritance for sub-techniques (inherited_tactics=true flag) and per-item next_calls (D3FEND bridge when attack_reference_id present, sibling-technique search by tactic, parent lookup for sub-techniques). Free: 30/hr (1 per item), Pro: 500/hr. Returns {results [{technique_id, status (ok|not_found|invalid_format), technique, error}], total, successful, failed, partial, summary}.
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  • Get full details for a specific quantum computing paper by its arXiv ID (e.g., "2401.12345"). Use after searchPapers or getLatestPapers when the user wants to dive deep into a specific paper. Returns: complete abstract, all authors, publication date, AI-generated tags with reasons, hook (one-line summary), methodology, gist, and key findings. Requires a valid paper_id from search results. Returns error if not found.
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  • Load fundamental workflow for valuation, cash flow, margins, balance sheet. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about company valuation, "is X a good buy", financial health, debt levels, profitability ratios, revenue trends, earnings quality, or any deep-dive company analysis. Can be combined with other workflow tools.
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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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  • Unified search across a workspace or share — ONE query, results GROUPED BY TYPE into buckets (files, metadata [workspace only], comments), each independently paginated and health-reported. Call action='describe' for the full action/param reference. This is the grouped SUPERSET; for a single result type prefer the narrower tools: `storage action=search` (files only), `metadata action=search` (lexical metadata fields only). The code-mode `search` tool searches the API endpoint catalog, not your content.
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  • Search or browse the GBIF backbone taxonomy. Accepts scientific name fragments, rank filters, and higher-taxon constraints. Useful for exploring what species exist under a higher taxon (e.g., "list all families of Coleoptera"), for simple name-fragment searches, or when gbif_match_species returns too narrow a result. kingdom, family, and genus scope the browse to a higher taxon: each is resolved to its backbone key before the search runs, so the narrowest one supplied is what scopes, an alternative name resolves to the taxon it is a synonym of, and a name that matches no backbone taxon at that rank fails rather than returning the whole index. Names are capitalized as GBIF writes them ("Paridae", not "paridae") and are matched exactly, not fuzzily. Paginated — use limit and offset to walk through results.
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