"A search engine called Brave Search" matching MCP tools:

• archive_search

  Colour Memory

  Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.

  Connector

• list_dataflows

  Statec Lu

  Browse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an `id` (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass `query` to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).

  Connector

• run_knockout_search

  GleanMark Trademark Search

  Run an examiner-style knockout search with scoring via the unified knockout engine — the same engine the GleanMark product uses. This is a PURE USPTO conflict search over 14M trademark records (exact, phonetic, trigram, component words, coordinated class expansion, doctrine of foreign equivalents, design codes) with mark-similarity and commercial-overlap scoring. Returns 4-tier risk-grouped results (very_high/high/medium/low) with confusion scores, plus a dead-mark "naming territory" sample. The top-line verdict is calibrated four-tier — CRITICAL CONFLICTS / ELEVATED RISK / MODERATE RISK / LOW RISK — with a one-line reason, so multi-name shortlists rank meaningfully. ALWAYS pass goods_description when the user has told you what they sell — the risk bands score goods/services relatedness, so an identical mark in a related-goods class reads VERY_HIGH only when the goods are supplied (class-only scoring understates it). It does NOT check domain availability and does NOT run a brand/web availability check — for that, use check_brand_availability instead. Most searches finish in under a minute; before calling, give the user a one-line heads-up that it may take up to a minute. Optional owner_name adds portfolio context — shows the applicant's existing marks in searched classes.

  Connector

• chieflab_suggest_next_move

  ChiefLab

  P75 — Next Move Engine. USE WHEN measurement just came in (chiefmo_post_launch_review fired automatically at 24h, OR the user manually called it) and you want to know what the operator should do next. Reads metrics + the original launch's brief and emits a deterministic suggestion: {kind: 'follow_up_email' | 'founder_dm' | 'thread_reframe' | 'landing_iteration' | 'lessons_learned_post' | 'seo_title_test' | 'wait' | 'noop', priority, reasoning, draftBrief: {channel, headline, body, cta?, recipients?}, measurementGroundingFacts}. Pure-function; same input → same output, no LLM. Pair with chieflab_create_next_move_action to turn the suggestion into an approval-gated draft.

  Connector

• web_search_multilang

  gapup-mcp

  Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.

  Connector

• archive_search

  Colour Memory

  Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.

  Connector

• engine_info

  Backtest360

  Engine version, API contract number, and health. Free (not quota-counted). Call once at the start of a session to confirm the engine is reachable and which contract it serves.

  Connector

• grep

  Layven

  Search file contents with a regular expression (RE2 syntax, no backreferences or lookaround). Omit both path and prefix to search every text file in the workspace; pass prefix to search one folder, or path to search a single file. Returns matching lines with 1-based line numbers and surrounding context; line numbers are display-only, never include them in edit's old_str. Matches within single lines only. Use grep to locate exact text; use read to view the surrounding region.

  Connector

• create_campaign

  xmagnet

  Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.

  Connector

• search_finnish_statutes

  Velvoite — EU Financial Regulatory Compliance

  Return Finlex search URL for Finnish statute keyword search. Finlex has no public JSON search API — returns the search URL for Claude to fetch. Args: query: Search terms in Finnish or English (e.g. 'kilpailukielto', 'non-compete').

  Connector

• archive_search

  Colour Memory

  Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.

  Connector

• proximens_geo_list_categories

  Proximens Oracle

  List the GEO principle taxonomy of the Proximens GEO Engine with a live count of high-confidence principles per category. INPUT: none. RETURNS: JSON with a categories array of {category, count, description} sorted by count, plus a reconciled total that matches get_stats.total_principles. Categories: technical, structured-data, ai-search, content, e-e-a-t, freshness, multimodal, user-signals, performance, query-intent, internal-linking, mobile, other. USE WHEN you want to discover which categories exist before narrowing a search_principles call with the category filter.

  Connector

• fetch

  mcp

  Fetch the full Quantustik signal + forecast writeup for one ticker. Paired with search — call search(query) first to find the ticker's id, then fetch(id) here for the full readable content. Also accepts a bare ticker symbol typed directly (id need not come from a prior search call). Args: id: Ticker symbol as returned by search, e.g. "NVDA". Returns a dict with id, title, text (a plain-text signal/forecast summary suitable for quoting or summarizing), url, and metadata (verdict, conviction, generated_at).

  Connector

• search_legislation

  bill-commons

  Search FULL BILL TEXT -- not just known-bill-number lookup. `q` is matched against titles, descriptions, AND ingested document text via Postgres websearch_to_tsquery (supports "quoted phrases", OR, and -exclusion, same syntax as a search engine), with a fuzzy pg_trgm title-similarity fallback when the exact query has no hits. `q` can ALSO be a bill number ("HB 123", "H.B. 123", "hb123" all match) and that fast path is tried first. Optionally filter by jurisdiction (two-letter state code or name), chamber, and status. For a curated cross-state slice of a subject (e.g. "every AI bill in the country") rather than an ad-hoc keyword search, call list_topics first -- its membership rules also match on structured subject tags this full-text search does not see.

  Connector

• find

  Fast.io

  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.

  Connector

• audit_brand

  TOP GUN GEO-Lens

  Full brand visibility audit across LLM-indexed sources (Brave + Exa, 10 results). Returns a visibility score (0–100), score label, top 5 citation URLs, LLM index status, and 6 actionable GEO recommendations. Costs $1.50 USDC. For a quick snapshot at $0.05 use geo_quick_check.

  Connector

• procedures

  Clinicaltables

  "What is the procedure called [X]" / "medical procedure name lookup" / "patient-friendly name for [surgery]" — search clinical procedure names (NLM curated, ~7k entries). Returns short names like "Colonoscopy", "MRI of brain". Patient-facing language; use for forms or intake screens. For billing codes use icd10cm or a CPT source instead.

  Connector

• get_engine_schema

  reefapi-mcp

  COMPACT overview of ONE engine: every action with its description, required params and what it returns — but NOT the full param detail (kept lean so a 90-action engine stays token-cheap). Call this after search_engines to pick the right ACTION, then get_action_schema(engine, action) for that action's full params before call_engine.

  Connector

• get_catalog

  reefapi-mcp

  The FULL ReefAPI catalog — EVERY engine with its one-line title, grouped by category. This is the whole menu (≈ a few thousand tokens); SCAN IT AND PICK THE BEST ENGINE YOURSELF. You are an LLM, so you match the user's intent semantically — across ANY language, typo, or phrasing — far better than a keyword search can. Use this whenever search_engines didn't surface the right engine (or to be sure you didn't miss a better one). After you pick: get_engine_schema(engine) -> get_action_schema -> call_engine.

  Connector

• collection.create

  DocImprint

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

  Connector