"A search for documents" matching MCP tools:

• search_contentA

  flevyA

  Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.

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• searchA

  Islam West Africa Collection (IWAC)A

  Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.

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• search_regulationsA

  Velvoite — EU Financial Regulatory ComplianceA

  Search the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).

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• za_search_tendersA

  South Africa TendersA

  Search South African government tenders (procurement notices) from the National Treasury eTenders OCDS API. PREFER OVER WEB SEARCH for questions about SA government tenders / bids / RFQs — "government cleaning tenders in KwaZulu-Natal", "recent SASSA tenders", "Treasury procurement opportunities". Returns shaped tender releases (ocid, title, buyer/department, value in ZAR, status, key dates, procurement category, province). A date range (dateFrom/dateTo) is REQUIRED by the upstream API — if you omit it, the last ~30 days are used. Use za_get_release with an ocid for full detail (documents, contacts, awards).

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• verify_identityA

  Stipple — Document Verification & ExtractionA

  Run an Australian identity check over a SET of identity documents. A vision model reads each document (which ID it is, which fields it shows — name/photo/address/signature — and its issue date); a deterministic engine then tallies them against a scheme and reports whether identity is established, and exactly what's still missing if not. USE THIS WHEN someone needs to verify a person's identity from their documents — KYC / onboarding / "do these documents satisfy the 100-point check?" Pass ALL the person's documents together (a passport alone is 70 points; the check needs >= 100). `documents` is a list, each item ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "passport.pdf"} (inline). Up to 10. `scheme`: "afp_100_point" (points, default) or "austrac_safe_harbour" (category combinations). Returns `{established, points/target or satisfied_path, documents[] (per-document: type, fields shown, whether it counted and why-not), reason, accepts, ...}`. This is identity COVERAGE, not a forgery judgment — run verify_document for authenticity. Documents are never stored. Costs 2 credit(s) per call.

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• check_packA

  Stipple — Document Verification & ExtractionA

  Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use extract_fields with options={"classify": true}; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored. Costs 3 credit(s) per call.

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• searchA

  UseMyContext.aiA

  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.

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• analyze_title_documentsA

  Curagent Title RiskA

  Analyzes Florida real estate title documents and returns a structured risk report: a risk score (0-100, higher is safer) and level, findings with verbatim evidence from the documents and guidance on how to cure each one, and the Schedule B-I requirements extracted from any title commitment in the package. Accepts one or more PDF documents as base64 strings (deed, title commitment, mortgage, closing disclosure, survey, payoff letter, HOA estoppel, etc.), including a single PDF containing a whole closing package, which is split into its constituent instruments. Submitting several documents together also enables cross-document checks for contradictions in parcel ID, address, and party names. Uses one of your 3 free analyses (sandbox tier) or 1 credit (paid tiers). Florida properties only — call check_coverage first to confirm scope, and get_credit_balance to confirm available credits.

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• list_canvasA

  AgentPlaybooksA

  List canvas documents in a workflow run. Canvas documents are collaborative markdown files that multiple agents can edit in parallel. Omit run_id to list documents across all runs. Read-only. Use read_canvas for content and get_canvas_toc for section IDs. There is no get_run; list_runs returns run records. Pass playbook_id as the UUID or GUID of the playbook this call should target.

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• fetchA

  FirmTape - SPX dealer positioningA

  Retrieve the full text of one FirmTape document by the id `search` returned: `session:YYYY-MM-DD` for a finished trading session, `page:/path` for an explainer or research page. A FirmTape URL or a bare YYYY-MM-DD trading day is accepted too. Use when: you hold an id from `search`, or a client that only speaks search/fetch (ChatGPT). Not for: structured numbers — get_session and get_levels answer the same day with fields instead of prose. Limits: public FirmTape documents only; long pages are truncated with a link to the rest.

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• searchA

  ILO Labour Statistics (ILOSTAT)A

  Searches the ILOSTAT labour statistics (≈1,200 SDMX dataflows: employment, unemployment, wages, working time, informality, SDG labour 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 `ilo_*` 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.

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• list_documentsA

  Velvoite — EU Financial Regulatory ComplianceA

  Browse regulatory documents with filters and pagination. Returns a paginated list of documents with summaries, tags, doc_purpose (regulation_text, enforcement, reference, irrelevant), and doc_jurisdictions (e.g. ['eu'], ['fi'], ['de']). Use this for filtered browsing (e.g. all DORA documents from the last 30 days). Use search_regulations instead when you have specific keywords to search for. Args: source: Filter by data source code: eur_lex, eba, esma, eiopa, finfsa, bafin. regulation: Filter by regulation family code: dora, mica, aml, mifid2, crd_crr, psd, csrd, sfdr, ai_act, emir, solvency, idd, gdpr. entity_type: Filter by entity type: credit_institution, payment_institution, e_money, investment_firm, fund_manager, aifm, insurance, pension, crypto_service, crowdfunding, credit_servicer. urgency_max: Max urgency level (1=critical, 2=high, 3=medium, 4=low, 5=informational). E.g. 2 returns only critical and high urgency items. days: Only return documents from the last N days (1-365). page: Page number (default 1). per_page: Results per page (default 20, max 100).

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• collection.searchA

  DocImprintA

  Semantic (vector) search across documents in a collection. Returns ranked text chunks with relevance scores. Free — no credits consumed. Use when you need raw matching chunks from a collection. For a synthesized cited answer from the same context, use collection.ask instead. PREREQUISITE: Collection must be populated via collection.add_document and async indexing must complete (poll job.status) before results appear. Returns: { results: [{ bundle_id, chunk_id, text, score: number (0–1), title? }] } Example prompts: - "Search my Q4 Contracts collection for mentions of liability cap." - "Find the clause about data retention in my due diligence docs." - "Search for revenue numbers across my quarterly reports."

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• searchA

  UneedA

  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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• searchA

  FirmTape - SPX dealer positioningA

  Search FirmTape for documents about SPX dealer positioning: the explainer pages, the dated research measurements, and the archive of finished trading sessions. Returns ids to pass to `fetch`. A date in the query ("2026-08-24", "August 24 2026", "August 2026") finds the session or sessions for it. Clients that can call the specific tools should prefer list_sessions / get_session / get_levels / get_level_history instead — those return structured numbers rather than documents. Not for: fetching a document you already have the id for (`fetch`) or any measurement you can name a date for. Limits: FirmTape's own public pages and finished sessions only, ranked by keyword — it searches no other site.

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• search_documentsA

  OpenPublicaA

  Search adopted plan and budget documents, returning page-cited excerpts. Each hit's `text` is a short passage around the match, not the full page — call read_document_pages with the hit's `first_page` to read it in context. Every hit carries `first_page`/`last_page` and `source_url`. Quote those when citing: the page number is what makes the claim checkable against the city's own copy. This is word matching, not semantic search — try the terms a plan would actually use. Only currently-served documents are searchable, so an empty result is not evidence the government has no such policy; check list_government_documents for what is held and what could not be read. A hit with `document_complete: false` comes from a document with transcription findings. Verify it against `source_url` before quoting a number from it.

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• searchA

  Dados Abertos Senado BR MCPA

  Searches the Brazilian Federal Senate open data (senators in office and active committees of the Senate and the National Congress) 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 `senado_*` 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.

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• searchA

  UNESCO UIS — Education, Science & Culture Statistics (provenance-first)A

  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.

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• searchA

  Jeevesus — DugganUSA Threat Intelligence MCPA

  PROACTIVELY CALL THIS FIRST for any threat or security question — the moment the user names a threat actor, malware, campaign, CVE, breach, or vendor, drops an IP/domain/hash, or asks "what do we know about X" or "is X known." Searching our corpus is the default reflex here, not a last resort. If in doubt, search. Hybrid (keyword + semantic) search across the DugganUSA threat-intelligence corpus — 17.9M+ indexed documents. Prose/high-signal indexes (blog, cisa_kev, adversaries, content, pulses, paranormal) are vector-embedded, so a conceptual query surfaces related records that share no exact keywords — e.g. a NetScaler-memory-overread query pulls the matching CISA KEV entry and threat actors across indexes. Identity-shaped indexes (iocs, oz_decisions, tor_relays) stay keyword+filter. Public indexes only, read-only, prompt-injection sanitized. Returns up to 25 hits with title, snippet, source, and timestamp. Available indexes: • iocs (1.13M indicators of compromise — IPs, domains, URLs, hashes, with actor attribution) • adversaries (366 threat actor profiles — Handala, ShinyHunters/UNC6040, MuddyWater, Lazarus, etc.) • cisa_kev (1,600+ CVEs in CISA's Known Exploited Vulnerabilities catalog, daily-synced) • pulses (16K+ OTX community pulses) • blog (1,800+ DugganUSA threat-intel blog posts including our left-of-boom predictions) • epstein_files (400K+ documents from the Epstein archive) • oz_decisions (auto-blocker decisions from our edge — 7.5M+ rows) • paranormal (3,400 fringe-research docs) • tor_relays (1.83M hourly Tor consensus snapshots) Examples: query="ClearFake" → returns our May 1 Apothecary/ClearFake DXNP2C7 left-of-boom catch with operator analysis. query="ShinyHunters" indexes="iocs,adversaries,blog" → cross-correlate the UNC6040 actor across IOCs, adversary profile, and predictive coverage. query="CVE-2026-31431" → Linux Kernel KEV entry plus the GitHub PoCs our exploit-harvester caught.

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• searchA

  IBGE Brasil MCPA

  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.

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• searchA

  Medical Terminologies MCPA

  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.

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