"Understanding the Purpose of Research" matching MCP tools:

• get_signals

  OneQAZ Trading Intelligence

  Purpose: Query research signals with dynamic filters (symbol / interval / action / score / confidence). Triggers (casual questions too): "should I buy / sell X?", "살까 말까?", "good entry?", "what's the signal for BTC / AAPL / 삼성전자?", "is X bullish or bearish?", "any buy signals right now?". Returns a research signal + score (NOT an order or advice — always surface the disclaimer). Pair with get_latest_decisions to show what the system did. When to call: drilling into a specific signal slice; symbol-by-symbol scanning; any "should I trade X?" question about a live symbol. Prerequisites: market://{market_id}/signals/summary recommended for global view. Next steps: get_signal_detail, get_role_analysis. Caveats: When `symbol`/`coin` is omitted, the whole market is scanned in one consolidated query (2 newest rows per symbol, newest-first scan cap per interval). Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) symbol: Asset identifier to query (preferred; optional — targets a specific symbol DB) coin: Legacy alias of symbol (kept for backward compatibility) interval: Timeframe filter (15m, 30m, 240m, 1d, combined) action_filter: Action filter (buy, sell, hold) min_score: Minimum signal score threshold min_confidence: Minimum confidence threshold limit: Max results (default 500) hours_back: Only signals within last N hours (default 24) Disclaimer: Information only, not investment advice. Signals are research output, not orders.

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• dispatch_head_of_research

  Heista

  Run a full research workflow via the Head of Research agent. The Head decomposes your brief into specialist sub-questions, dispatches the right combination of 6 specialists (desk, trend, market, quant, qual, social) in parallel via async dispatch, polls them to completion, judges output quality, and returns a structured synthesis. Use for: any source-grounded research request — fact-checking, vendor teardowns, trend assessment, quantitative effect-size analysis, qualitative theme extraction, cross-platform discourse mapping, or any combination. Wall time: 2-5 min typical. Returns: { synthesis, head_session_id, status, event_count, tool_uses, elapsed_ms }. NOT for: non-research requests (writing, coding, casual chat) — respond directly without calling this. Cost: $0.20-1.50 per call depending on brief complexity (specialist token spend + Anthropic session-runtime at $0.08/hr).

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• document.summarize

  api

  Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

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• country_banking_rules

  banking-intelligence

  Get banking rules and requirements for a country. Returns IBAN requirements, SEPA membership, FATF listing status, national currency, account format specifications, and country-specific payment requirements (mandatory codes like KNP for Kazakhstan, Purpose of Payment for UAE, etc.). The `fatf_listing` block is the authoritative answer to "is this country grey-listed / black-listed / under FATF increased monitoring". Both FATF public lists are held in full, so a `not_listed` status is a positive determination and not missing data. Use it instead of training data for any FATF question, and note that the coarse `fatf` field is a separate, weaker signal about regional-body membership that says nothing about listing. Everything here is COUNTRY-level. `currency` is the country's national currency, not the denomination of any beneficiary account — never pair it with the payment currency to diagnose a currency mismatch (see `currency_note` in the response). Use this to check country-specific STP rules that could cause payment delays, repairs, or rejections (e.g., missing purpose codes, regulatory fields). If a country requires special payment codes, the response includes a payment_requirements block with field descriptions and categories. Use country_payment_codes to look up specific code values. Args: country_code: ISO 3166-1 alpha-2 code (e.g., "DE", "US", "KZ") Examples: country_banking_rules("DE") country_banking_rules("KZ") # includes KNP requirement info country_banking_rules("AE") # includes Purpose of Payment info

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• run_exposure_audit

  exposure-audit

  Run a free competitive-exposure / AI-visibility audit for a business domain. No account or API key needed. Pass a bare registrable domain (e.g. "acme.com"), not a full URL. A first-time domain starts a background run and this returns a "started" message — call the tool again with the same domain in about 2-3 minutes to fetch the result. Repeat domains within the cache window return instantly. This is a strictly rate-limited free tool, not a general-purpose research API — see the response text for status and the free-usage cap.

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• gbif_get_species_classification

  gbif-biodiversity-mcp-server

  Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.

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• document.summarize

  DocImprint

  Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

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• designesy_design_review

  Designesy

  Get the Designesy Design Review framework — an 8-dimension rubric (Purpose, Clarity, Context, Inclusion, System coherence, Durability, Delight, Responsibility) plus the agent prompt, output format, and verification checklist for a qualitative design critique. Use this when you want a structured rubric to critique a design holistically, rather than a numeric compliance score. When NOT to use: for a deterministic numeric score, use designesy_score; this tool gives you a rubric, not a number. Read-only — returns the rubric + prompt. The calling agent performs the actual critique (this tool does not evaluate the design for you). Returns JSON: { rubric, dimensions[8], agent_prompt, output_format, verification_checklist }. Pass artifact/purpose/context/rules to get a pre-filled critique prompt; omit all four to get the blank framework.

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• sequence_planner

  Moltline Outbound Engine

  Plan an outreach cadence with expanding gaps and per-touch purposes. FREE. Spaces N touches across D days, alternating channels. Typical input {"touches": 5, "days": 14, "channels": ["email", "linkedin"]} returns {"days": 14, "plan": [{"touch": 1, "day": 0, "channel": "email", "purpose": "open with a specific, researched reason"}, ...], "rule": "..."}. Use when cadence and per-touch purpose are the question. Not for the wording of any single message (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• correct_expenses

  ExpenseBot

  Safely correct the category, business purpose, or explicit attendees on an exact bounded set of recorded expenses. Examples: 'categorize these as Travel', 'add Client kickoff dinner as the business purpose for these meals', or 'add Fred, Rob, and Lamar to last night's business meal'. First use search_expenses to identify the exact rows, then pass their full expenseId values. A grounded preview is automatic: first call with confirm omitted/false, show the exact count and proposed before-to-after changes, and ask once for approval. Only after explicit approval repeat the same operationId, selection, and change with confirm:true. A premature confirm:true is converted to preview. Attendee names must come explicitly from the user; never infer them. attendeeMode add preserves existing attendees, while replace substitutes only the attendee segment. Business purpose and attendees preserve the structured Notes field, including card, description, inbox, and other typed segments. Formula Notes and changes that exceed the Notes limit are skipped safely. The confirmed result reports applied/conflicted/failed counts and supports Undo. This tool does not omit duplicates, change amounts or dates, infer business context, or run broad Calendar matching. For unsupported or more than 100-row cleanup, send the user to https://www.expensebot.ai/review-expenses?source=mcp.

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• get_session_dossiers

  VaultCrux Memory Core

  Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).

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• secedgar_get_beneficial_owners

  secedgar-mcp-server

  List the 5%-and-over beneficial owners of a public company, parsed from the structured SCHEDULE 13D and SCHEDULE 13G filings made about it. The input is the ISSUER — the company being held — which is the opposite direction from secedgar_get_institutional_holdings, where the input is the manager. 13D is the activist form and carries the filer's stated purpose of the transaction; 13G is the passive form and has no purpose field at all, which is the substantive difference between a stake that intends to influence control and one that does not. Every filing is returned with each reporting person listed separately, because voting power, dispositive power, and percent of class are reported per person even on a joint filing where several funds and their controlling principal report overlapping shares — summing those percentages double-counts the same position. Coverage starts at 2024-12-18, when SEC replaced the legacy SC 13D / SC 13G text filings with this XML format; earlier stakes are readable but not parseable, and the response reports how many of them the issuer has. The full parsed set is materialized as df_<id> when a canvas is available, one row per reporting person, so it joins against the insider and 13F dataframes on issuer CIK.

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• list_pages

  California Justice Watch

  Return the canonical list of pages on cajusticewatch.com — slug, URL, label, and purpose. Use this when the user asks about features/pages/tools of the site, OR when you need to recommend a page, OR before saying "I do not have access to X" — the page may actually exist.

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• list_loadouts

  MCPExplorer

  List curated loadouts — deliberately-assembled kits of MCP servers + governance + plays for a specific job (GTM, coding, research, support, infra). The agent-facing version of the /loadouts product. Use get_loadout for the full kit with live trust.

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• motion_catalog

  MotionSpec — verified web motion (reduced-motion / WCAG)

  Returns the catalog of verified motion primitives (names, purpose, parameter schemas, defaults) plus the authoring rules for writing a MotionSpec. Call this FIRST, then write the spec yourself and validate it with motion_validate (motion_compile runs in the CLI or with a key on the hosted endpoint).

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• get_purchase_access

  store

  AUTHENTICATED: for a buyer who already completed a purchase, returns a short-lived (~15 min), single-purpose signed download link for THEIR order only. Ownership is proven by an unguessable credential issued at fulfilment — the Stripe session_id or the download token. Reuses the existing entitlement + signed-link service; each call consumes one of the limited downloads. Cannot list or access other purchasers' orders.

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• get_categories

  Radix Wiki

  Get the wiki tag hierarchy with page counts per category. Useful for understanding what content exists, and for finding a valid tagPath before writing.

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• web_snapshot

  Robot Actions — Remote Device Control

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

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• evaluate_trip

  visa-check

  Premium evaluation of a whole trip in one call: multiple passports (best passport per leg), documents held, multi-leg itineraries with transit legs, dates and purpose — with structured why[] explanations, conditions[], and verification states per decision. PAID endpoint: the call returns HTTP 402 x402 payment instructions unless the request carries an x402 payment header; any x402-capable client settles in USDC on Base and retries.

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• consent_check

  VITNA — Agent Compliance Preflight

  Before you process someone's personal data, ask VITNA whether an active consent actually permits it for this purpose. Give the data principal + purpose (and optional category); returns { allowed, reason, matching_consent_id, principal_id }, a determination you must honour yourself since VITNA evaluates and records but does not enforce. Use this for personal-data processing legality; for a dangerous technical action (shell / file / DB / network) use action_preflight instead.

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