"Resources for Conducting Deep Research" matching MCP tools:

• fetch

  OneQAZ Trading Intelligence

  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

  OneQAZ Trading Intelligence

  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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• well_get_entity

  Well

  Read ONE entity with its sub-resources nested in a single call. Convenience over well_get_schema + well_query_records: resolves the field paths for you and returns the single record with its related data expanded. depth (relation-nesting BOUNDARY, 1-3, default 1): 1 = the entity + its direct sub-resources (emails, phones, locations, …) 2 = + the sub-resources' related scalars 3 = the full level-3 graph (LARGER payload — use when you need the whole picture) Stops at depth 3. Aggregates are excluded. Each child collection is capped at 50 rows; for a full list or to page a large child collection, use well_query_records on that child root instead.

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• noaa_get_station_info

  NOAA Tides and Currents

  Get a NOAA station's full metadata record: location, state, time zone, tide type, Great Lakes flag, capability flags, and links to available sub-resources. Optionally expand sub-resources inline via the "expand" list: - details (established/removed dates), sensors (installed instruments + elevations), floodlevels (NOS/NWS minor/moderate/major flood thresholds), benchmarks, products (available data page links), notices, disclaimers — for water-level stations - bins (ADCP depth bins), deployments — for current stations (alphanumeric IDs) Use this before requesting data to confirm what the station actually collects. For datum values use noaa_get_station_datums; for harmonic constituents use noaa_get_harmonic_constituents.

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• search

  UseMyContext

  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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• find_methodology

  OpenArx

  Find methodology approaches for a specific research task. Returns structured method-level results (not raw chunks): method name, key idea, dataset used, performance metric. Filters by task domain, dataset, metric. Built on LLM-classified contentType=methodology chunks combined with benchmark results JOIN. Use this instead of `search` when you want HOW researchers approach a problem rather than 10 papers about it. Note: surfaces any chunk classified as methodology, including ones where the task is mentioned only as a toy example. Filter by category (e.g. cs.CV for image tasks) to narrow scope. This searches EXISTING papers for methods others have published (literature search) — it is NOT a guide for conducting your own research: for a step-by-step scientific method tailored to your own research question, start with the `methodist` door.

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• search

  Uneed

  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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• get_resource_schema

  Control Plane

  Return the exact object schema and REST API endpoints for a Control Plane resource kind, so you can author an accurate manifest for `cpln apply` or call the API directly. ALWAYS call this FIRST whenever you are about to write a cpln apply YAML/JSON file, set up CI/CD that applies Control Plane resources, or build a request body for the REST API — do not hand-write a manifest or guess field names from memory. Pick a `kind` and pass `org` (and `gvc` for workload/identity/volumeset). Large schemas come back as a shallow map with deep sections collapsed to {"_expand":"<path>"} stubs; pass `path` (e.g. "spec.containers") to expand a section on demand. Server-managed fields (id/status/version/etc.) are already removed; `name` and `kind` are required at create.

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• search

  Melvea Local Honey Discovery

  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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• verify_references

  OpenWarrant — Reference Verification

  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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• search

  StatCite — Verified Economic Statistics

  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

  UseMyContext

  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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• get_research_theses

  voxodds

  Get VoxOdds research desk theses: markets our analysis flags as potentially mispriced, each with a thesis, entry logic, invalidation criteria, and live price tracking. Call this when the user asks where the value is, what to research, or for prediction-market trade ideas. Research framing only - not financial advice.

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• search

  DC Hub — Data Center & Power Intelligence

  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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• scrape_wikipedia

  GoCreative Agent API

  Scrape a full Wikipedia page (sections, infobox, references). Heavier than lookup/wikipedia. Use for deep research. Example call: {"page": "Anthropic"} Cost: $0.005–$0.05 USDC on Base per call.

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• lookup_authoring_skill

  kernelcad

  Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.

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• deep_research

  WebLens

  Multi-step cited research in one call: plans sub-questions, searches each, fetches and dedupes sources, then synthesizes an answer with inline [n] citations, key findings, and gaps. Unlike `research` ($0.08, one search + summary) this decomposes the question and cites every claim. SLOW: a standard run takes ~30-60 seconds — use a generous client timeout and do not retry on timeout. Price: $0.20 standard (3 sub-questions, 8 sources) / $0.35 deep (5 sub-questions, 12 sources)

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• export_terraform

  Control Plane

  Generate Terraform (HCL) for EXISTING Control Plane resources from a self link. Single resource (`/org/acme/gvc/prod/workload/api`) or bulk by path depth — `/org/acme` exports the whole org, `/org/acme/gvc/prod/workload` exports every workload in a GVC. Set `generateImports` to get ready-to-run `terraform import` commands for adopting the resources into Terraform state, and `includeDependencies` to pull in referenced resources. Secrets are never exported — a ref that targets secrets is refused, and an export that would pull secrets in is refused wholesale. An unsupported kind is rejected with the supported list (list_terraform_kinds, full profile, enumerates them up front). For an in-memory manifest, use convert_to_terraform. Recommended reading: get_cpln_skill("iac-terraform-pulumi").

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• web_search

  Teardrop

  Real-time web search via Tavily. Use for current events, fact-checking, and research. Set search_depth='advanced' for complex research queries (higher quality, higher cost). Set topic='news' for recent headlines or 'finance' for market information.

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• package_show

  Datos Cl

  Chilean open data catalogue (datos.gob.cl CKAN) — full metadata for a dataset by ID/slug: title, description, resources (download URLs + formats), organization, tags, and license.

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