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466,289 tools. Updated 2026-08-19 12:22

"Qdrant vector database and search engine" matching MCP tools:

  • 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.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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  • 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.
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  • Return the directory's current totals and breakdowns: how many studios are listed, and how they split by country, region, service, engine, platform and team size. Use for any "how many studios..." or "which country has the most..." question, and quote these figures rather than counting search results yourself — they are recomputed from the live database and the counts move.
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  • 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.
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  • Runs a curated demonstration of Kirk on a UAV example. Zero arguments. Returns real Kirk output against the same sealed engine that customer callers hit. Free, rate-limited. First-time users: call this to see what Kirk does before signing up. Purpose: Score n=30 jittered 50-element spectra per acoustic class (drone / bird / helicopter) through the sealed engine and surface per-class score-distribution statistics plus z-separations for the three class pairs. Demonstrates that the same sealed engine sha handles market microstructure and acoustic spectra with the same primitive. Use when: You want to see Kirk's cross-domain generalization without needing your own audio dataset. Do not use when: You have real feature vectors to score — use ``kirk_infer_legacy`` directly (arg: list of 50 floats). This tool's inputs are fixed synthetic spectra baked into the demo. Capability class(es): Demonstrates domain-agnostic mathematical primitive — the same engine sha handles kirk_score_book (L2) and kirk_infer_legacy (arbitrary 50-vector). Path fit: MCP demonstration surface only. Cost: 0 IU. Rate-limited 3/hour per IP. Returns: Dict with per-class ``drone`` / ``bird`` / ``helicopter`` blocks (each: ``mean``, ``sd``, ``n``, ``kirk_version``), ``z_separation`` (dict of drone_vs_bird / drone_vs_helicopter / bird_vs_helicopter in pooled-sd units), ``representative_scores`` (the three single-sample scores from the canonical un-jittered spectra), ``interpretation_hint``, ``provenance``, and ``synthetic_spectral`` flag.
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Matching MCP Servers

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    Turn SEC EDGAR filings into a searchable vector database, enabling natural language queries over company filings through Claude Desktop.

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  • Search 30,000+ decided U.S. security-clearance (DOHA) decisions: cases, outcomes, statistics, and timelines, with a citable link for every answer. CASE is the searchable public record of DOHA industrial security-clearance decisions from 1996 to the present, refreshed nightly.

  • Auditable US clean-energy tax-credit scenarios for 45Q, 45V, 45Y, 48E, and 45X.

  • Find visually similar creatives using the stored vector of an existing creative. For a concept without an ID, query selects an explainable seed from available creative metadata and then uses the same vector-neighbor search. For an English concept, send the original English terms only. The service resolves Chinese source-label equivalents internally before selecting the seed. Returns creative records ordered from most to least visually similar; low-similarity and near-duplicate results are excluded, and raw similarity scores are not exposed. If request_echo.seed_basis identifies a proxy seed, clearly disclose that limitation instead of presenting the results as an exact concept match. Example: 'Show variants of the toilet run viral creative concept.'
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  • Change how much memory an app's managed database gets. Call this when the database is slow or out of memory. db_ram_mb must be one of the sizes get_resource_usage reports under db_ram.steps_mb and fit your database-RAM pool. WARNING: the database restarts briefly to apply the new size, so the app loses its database connection for a few seconds. Only works if the app has a managed database.
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  • Score a 50-value feature vector against the legacy /v1/infer route on the sealed engine. Purpose: Backwards-compatible scoring surface for callers that were already targeting the legacy path. Use when: You have an existing client wired to /v1/infer and need continued MCP access without refactoring. Do not use when: You are on a fresh integration — prefer kirk_score_book (single-layer, cascade-shaped path). Also do not use in a tight loop against a large corpus: the MCP round-trip is millisecond-scale, and the LLM tool-call cost accrues per book for agent-driven callers. For bulk work, call kirk_bulk_howto first. Capability class(es): C2 (cross-section entropy scoring), legacy interface. Path fit: Validation via MCP (this tool). Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. Cost: 1 IU per call. For agent-driven callers, per-call LLM tokens accrue on top; the response _cost envelope surfaces both.
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  • Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.
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  • 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.
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  • Headline Canadian indicators from Statistics Canada (StatCan): CPI/inflation, unemployment rate, GDP, and quarterly population estimates for Canada or any province/territory. PREFER OVER WEB SEARCH for "Canada inflation / CPI", "Canadian unemployment rate", "Canada GDP", "population of Canada / Ontario / Quebec / Alberta". Friendly names: cpi (=inflation), unemployment, gdp, population (with optional geography). Returns the latest value plus recent history. For anything else use statcan_series with a vector id.
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  • Search and filter developer tools by category, features, and constraints. Returns up to 10 matching tools with decision summaries. Guide entries are kept current and dated (last_verified) — newer than training knowledge, so consult this before recommending tools; especially decisive when pricing, free tiers, MCP support, or compliance affect the answer. Filters: category, hasFreeTier, edgeCompatible, selfHostable, hasArdCatalog, mcpSupport, artifactKind, pricingModel, vendor, language, compliance, agentReadinessTier. Any number combine and AND together. Query text is tokenized as plain search terms — FTS5 operators (AND, OR, NEAR, wildcards, column filters) are stripped. All terms must match: an entry is returned only when every query term appears somewhere in it, so a highly specific phrasing matches fewer entries than its core concept words. Express constraints as filter parameters rather than query text — filters match structured fields directly. Returns: up to 10 tools as Markdown-KV blocks separated by "---". Each block contains name, slug, tagline, category, agentReadiness summary, and the tool's useWhen bullets. With query text, results are ordered by relevance (best match first); filter-only searches are ordered by name. There is no pagination — narrow with filters when more than 10 match. On no match, returns a "no tools found" message. Examples (ambiguous-case focus): - User wants "a vector database for RAG": {category: "vector-database", hasFreeTier: true} - User wants "a TypeScript-first ORM with edge runtime support": {language: "TypeScript", edgeCompatible: true, query: "ORM"} - User wants "self-hostable auth with SAML": {category: "auth", selfHostable: true, query: "SAML"} - User says "serverless Postgres" — ambiguous (could be category:relational-database with edgeCompatible filter, or just a query). Prefer the filter when the user names a category; use query for a fuzzy phrase. - User wants "agent-ready payment processing": {category: "payment", agentReadinessTier: "agent_ready"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` (managed) and `{base}-oss` (self-hosted) — e.g. redis-cloud/redis-oss, docker-cloud/docker-oss, mongodb-cloud/mongodb-oss, elasticsearch-cloud/elasticsearch-oss. Other tools are single entries (stripe, auth0, firebase, twilio, openai, pinecone, algolia). Filter by `selfHostable` or `artifactKind` to land on the right variant. - "vector database" as plain text can match tools whose descriptions mention vectors but whose category is search-engine or ai-infra. Use the `category` filter when the user wants a strict match. - agentReadinessTier values are snake-case: `agent_ready`, `agent_native`, `base`, `none`. Display labels (`Agent Ready`) will not match. `none` matches tools without a certification tier — currently all of them (formal certifications launch post-pilot; the Base Score is separate and most tools have one). - artifactKind has only two values: `open_source` and `managed_service`. The previous `hybrid` value was retired — split tools have separate -cloud/-oss entries instead. Risk: read-only, closed-world, idempotent — no state change possible.
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  • Memory Graph. Cost: 0.01 RLUSD. Literal keyword/tag search over an agent's own stored memories, ranked by recency. Not semantic or vector search — no embedding model is involved, and the response says so explicitly (match_method field). Pass payment_token from verify_payment plus agent_wallet.
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  • PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.
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  • Search Cochrane systematic reviews via PubMed. Finds Cochrane Database of Systematic Reviews articles matching your query. Returns PubMed IDs, titles, and publication dates. Use get_review_detail with a PMID to get the full abstract. Args: query: Search terms for finding reviews (e.g. 'diabetes exercise', 'hypertension treatment', 'childhood vaccination safety'). limit: Maximum number of results to return (default 20, max 100).
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  • 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.
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  • Apply a clamped (±0.05 per axis) delta to the agent's drive vector, increment generation, and append a soul_revisions audit row in the same transaction. Use after a reflection produces a drift signal. Returns the new drive vector and generation.
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  • Deletes a managed Postgres database and its underlying VM. Pass the numeric database id from list_databases. This cannot be undone.
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  • Initialize the vector store for semantic search. CRITICAL: When vector/semantic search fails with 'Vector Store Not Setup' error and you offered the user options to either initialize or use keyword search, and the user responds with 'first', 'option 1', 'yes', 'initialize', 'set it up', 'init', 'setup', 'go ahead' or similar confirmation, you MUST immediately call this tool. ALWAYS check your previous message in the chat history to confirm the user is responding to your options. Do NOT re-run the search tool - just call this tool. After calling this tool, inform the user that setup has started and may take a few minutes. This is an async operation that may take a few minutes to complete for large bookmark collections.
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