"Resources for Conducting Deep Research" matching MCP tools:

• verify_business

  BizVerify

  Confirm a specific, named business in one jurisdiction — the PRIMARY tool whenever the user wants to verify, check, confirm, or look up a company's existence, status, good standing, or details (e.g. "verify Acme LLC in Delaware", "is Acme registered in FL?", "I need to verify a company in Delaware"). If the user has verification intent but has not given the exact company name, ASK them for the name and use THIS tool — do NOT fall back to search_entities. Two tiers: quick (1 credit) returns existence + status + good-standing. Deep (15 credits, or 25 with force_refresh) adds entity type, formation date, registered agent, officers, principal address, and filing history. Deep is available in a subset of jurisdictions; requesting deep where unavailable returns a quick result with a reason. Requires authentication; deducts credits only on a successful match.

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• read_resource

  UK Legal Research

  Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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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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• usaspending_search_federal_accounts

  usaspending-mcp-server

  List and keyword-search federal accounts by agency identifier or title keyword. Returns account numbers, names, managing agencies, and budgetary resources. Use account_number from results as input to usaspending_get_federal_account for full budget detail. Use usaspending_list_agencies to look up agency_identifier codes (3-digit strings, e.g. "097" for DoD).

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• dataset_details

  Opendata Swiss

  Full metadata for one dataset (CKAN package_show) including its resources/distributions with download URLs. Use a dataset `name` (slug) or id from search_datasets. There is no datastore, so fetch `resources[].download_url`/`url` for the underlying data.

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• verify_references

  OpenWarrant — Document Verification Suite

  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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• download_resource

  Senzing

  Download workflow resources by name. Pass `filename` (string) or `filenames` (array); calling with neither returns the list of available resources (it does not fail). Available: sz_json_analyzer.py, sz_schema_generator.py, sz_verbatim_check.py, sz_routing_report.py, senzing_entity_specification.md, senzing_mapping_examples.md, identifier_crosswalk.json HTTP mode returns URLs; stdio mode returns `sz-mcp-coworker extract` commands. Supports batch via `filenames` array. Asset IDs are not stable across versions. If a previously-known ID fails to extract, call this tool again to obtain the current ID.

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• list_popular_indicators

  Owid

  List curated Our World in Data indicators (slug + title) for common categories: energy, climate, health, demographics, economy, food, education, environment, tech, politics. Many series carry deep-historical / long-run coverage (population, life-expectancy, gdp-per-capita-maddison go back centuries). Use the slug with fetch_indicator. Not exhaustive — visit ourworldindata.org for the full catalog.

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• createDeepResearch

  Parallel Task MCP

  Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.

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• image_personal

  Colour Memory

  Upload a portrait photo and receive a full personal colour analysis. Determines your seasonal type (Spring, Summer, Autumn, or Winter), colour depth (light, medium, or deep), and undertone (warm, cool, or neutral). Returns a curated palette of archive colours that genuinely suit you — each with full historical provenance and cultural context — plus colours to avoid. Uses Claude Vision for skin, hair, and eye analysis, then matches to the archive by CIEDE2000 perceptual distance. The photo is never stored. Example: a Deep Winter might wear Ottoman Carbon Ink while a True Spring suits Kogi Mango.

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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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• get_bundle

  DocImprint

  Retrieve metadata for an evidence bundle (ev_...) owned by your API key. Free — no credits consumed. Use verify_bundle for deep cryptographic integrity checks. Use get_bundle for quick status/metadata lookups. Returns: { bundle_id, source_url, mode, status: "pending"|"complete"|"failed", manifest_sha256, manifest_signature, signer_address, attestation_tx, attestation_at, eas_uid, parent_bundle_id, superseded_by, legal_hold: boolean, retention_until, created_at }

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• askew_intel_feed

  Askew x402

  Aggregated intelligence feed combining research findings, active security threats, and live staking APY snapshot in a single call ($0.005 USDC). Sources: ChromaDB research library + Guardian log + staking.db. Best for: broad situational awareness — replaces three separate calls. Requires x402 payment on Base mainnet.

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• zambo_ask

  Zambo AI Tools

  Ask any natural language question about the Zambo Stack — products, APIs, pricing, routing, how-to, use cases, or deep scan mode. Returns a precise structured answer optimized for AI agents. Best for: 'What does ZAMBRO do?', 'Which product should I use for lead generation?', 'How do I call the LeadSignal API?', 'What does Zambo Pass include?', 'How does ZAMBRO deep scan work?'. 20 calls/day free, no auth, no signup.

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• get_site_status

  BorealHost

  Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

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• get_cfpb_complaint_intelligence

  Stratalize Finance

  Use when assessing consumer finance risk, benchmarking complaint volume against peers, or conducting pre-acquisition due diligence on a financial institution. Returns CFPB complaint rollups by company and product — volume, issue themes, and response rate trends. Example: Regional Bank X — 847 CFPB complaints in 2023, 34% on mortgage servicing, complaint volume 2.3x peer median — elevated consumer protection risk signal. Source: CFPB Consumer Complaint Database synced data.

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• claidex_capabilities

  Claidex MCP

  Return the Claidex MCP feature map, configured storage/model providers, safety controls, resources, prompts, and tool counts.

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• image_personal

  Colour Memory

  Upload a portrait photo and receive a full personal colour analysis. Determines your seasonal type (Spring, Summer, Autumn, or Winter), colour depth (light, medium, or deep), and undertone (warm, cool, or neutral). Returns a curated palette of archive colours that genuinely suit you — each with full historical provenance and cultural context — plus colours to avoid. Uses Claude Vision for skin, hair, and eye analysis, then matches to the archive by CIEDE2000 perceptual distance. The photo is never stored. Example: a Deep Winter might wear Ottoman Carbon Ink while a True Spring suits Kogi Mango.

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• dataset_details

  Datagov Ie

  Full dataset record by id or slug (CKAN package_show), including its resources. Each resource has a download "url" (often PDF/CSV/XLSX) and a "datastore_active" flag; resources with datastore_active=true can be read row-by-row via datastore_query using the resource "id".

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