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260,835 tools. Last updated 2026-07-05 08:29

"A server for searching and retrieving information using keyword, semantic, or hybrid search methods" matching MCP tools:

  • Search the AI Tool Directory catalog (2,000+ AI tools) by keyword, use case, or category using hybrid semantic search. Returns ranked tools with slug, one-line description, pricing model, and rating. Use this to discover tools, then get_tool for full detail.
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  • Search the AI Tool Directory catalog (2,000+ AI tools) by keyword, use case, or category using hybrid semantic search. Returns ranked tools with slug, one-line description, pricing model, and rating. Use this to discover tools, then get_tool for full detail.
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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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  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • Search the ShippingRates database by keyword — matches against carrier names, port names, country names, and charge types. Use this for exploratory queries when you don't know exact codes. For example, search "mumbai" to find port codes, or "hapag" to find Hapag-Lloyd data coverage. Returns matching trade lanes, local charges, and shipping line information. FREE — no payment required. Returns: { trade_lanes: [...], local_charges: [...], lines: [...] } matching the keyword. Related tools: Use shippingrates_port for structured port lookup by UN/LOCODE, shippingrates_lines for full carrier listing.
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  • Check whether a factual claim is supported by a specific set of public evidence URLs that you already have. For each source, the tool performs a case-insensitive keyword match over the fetched page body, then marks that source as supporting the claim when at least half of the supplied keywords appear. Use this for evidence-backed claim checks on known pages, not for open-ended search, semantic reasoning, or contradiction extraction. The aggregate verdict is driven only by the per-page keyword support ratio. Fetched pages are cached for 5 minutes.
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  • Search PubMed and summarize biomedical literature — designed for AI health agents.

  • Search the web, images, videos, news, and local businesses with robust filters, freshness controls…

  • Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings. If you are retrying after a previous call returned no useful results, populate `prior_attempt` so the server can surface alternative wordings and learn what's missing from the docs.
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  • Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings. If you are retrying after a previous call returned no useful results, populate `prior_attempt` so the server can surface alternative wordings and learn what's missing from the docs.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Semantic search — match by meaning, not exact words. Uses vector similarity (cosine distance) over `text_pali` embedded with a multilingual MiniLM model. 🤔 **In most cases you should use `search_hybrid` instead** — it combines this semantic search with keyword search and ranks better. Use this tool only when you need: - Pure semantic results (no keyword influence) - Fine-grained `threshold` tuning (hybrid uses RRF which is harder to tune) - To debug what semantic alone picks up vs keyword ⚠️ Known limitations: - The index is **Pāli only** (English/Thai queries pass through the multilingual embedding but the model isn't tuned on Pāli) - English queries usually embed better than Thai (model is EN-primary) - For specific Pāli terms (`appamāda`, `dukkha`), exact match is better — use `search_by_keyword` instead - Pāli stock phrases recur in many suttas → similarity scores cluster; read the top 10, don't trust rank 1 alone
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  • General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)
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  • Search the ShippingRates database by keyword — matches against carrier names, port names, country names, and charge types. Use this for exploratory queries when you don't know exact codes. For example, search "mumbai" to find port codes, or "hapag" to find Hapag-Lloyd data coverage. Returns matching trade lanes, local charges, and shipping line information. FREE — no payment required. Returns: { trade_lanes: [...], local_charges: [...], lines: [...] } matching the keyword. Related tools: Use shippingrates_port for structured port lookup by UN/LOCODE, shippingrates_lines for full carrier listing.
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  • 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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  • Unified search across the registry and release content. Returns up to four sections — organizations, catalog entries (products + standalone sources folded into one list), curated collections (cross-org playlists), and releases with CHANGELOG chunks interleaved by relevance. Use `type` to narrow the surfaces you want and skip the expensive paths. For example, pass `type: ['catalog']` to look up a known entity by name (fast, registry-only); pass `type: ['releases']` when you only care about release content and want to avoid entity lookups. Omit `type` to search all four. Collections surface via two paths: a direct match on the collection's name/description (lexical in every mode, plus a vector match in hybrid/semantic mode) and a member rollup that includes every collection containing one of the matched orgs. Member rollups carry a list of result-set org slugs that triggered the rollup so a UI can render an "includes X" hint. Use `entity` (product slug / prod_ id OR source slug / src_ id) to scope release results to one catalog entry. Product identifiers expand to every source under the product. Use `organization` to scope to a whole org. Release retrieval defaults to hybrid (FTS5 + semantic vectors fused via RRF); it silently degrades to lexical when vector infra is unavailable and flags the result.
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  • Search a database of recipes using hybrid semantic search (dense + sparse) with reranking. The database contains ~50,000 recipes from Food.com covering a wide range of cuisines, meal types, and cooking styles. Recipes include nutritional information, difficulty ratings, and user ratings. Use natural language in the query to describe what you are looking for — cuisine, style, main ingredient, occasion, or mood all work well. Norwegian and English are both supported natively. Examples: 'quick Italian pasta for weeknight dinner' 'Swedish meatballs with gravy' 'healthy high-protein chicken bowl' 'easy chocolate cake for beginners' 'something with salmon and lemon' 'Indian curry chicken' 'traditional Norwegian kjøttkaker' 'hurtig pasta med kylling' 'enkel sjokoladekake' Args: query: What you are looking for — describe the dish, cuisine, main ingredient, cooking style or mood freely. Any language is supported. diet: Optional — filter by dietary requirement: 'vegetarian', 'vegan', 'gluten-free', 'dairy-free', 'low-carb', 'keto', 'paleo' max_minutes: Optional — maximum total time in minutes, e.g. 30 difficulty: Optional — 'easy', 'medium' or 'hard' servings: Optional — not used for filtering (servings vary), but include in query for scaling context, e.g. 'pasta dish for 6 people' limit: Number of results to return after reranking (default 5, max 20) Returns: List of recipes ranked by relevance. Each result includes rerank_score, rrf_score (hybrid fusion), title, total_time, difficulty, diet labels, ingredients, instructions, nutrition, rating, and source URL context.
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  • Get Google keyword traffic insights and related keyword suggestions for a URL. Returns an array of keyword suggestions. Each item includes text, monthly search volume, competition_level, competition_index, low_bid, high_bid, and trend. Required: url and language (for example en). Optional: location (for example US) for country-specific data; omit location for global results (default). Optional: min_search_volume (default 0) and intent (informational, navigational, commercial, or transactional). Cost = 20 tokens.
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  • Search worldwide patents by keyword, inventor, assignee, or phrase using Google Patents. Returns patent id, title, assignee, inventor, filing/publication dates, and a snippet. Args: query: Free-text query (e.g. "quantum error correction", "lithium battery anode"). max_results: Maximum number of patents to return (1-30, default 10).
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  • General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)
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  • Save a cognitive checkpoint for handoff to another agent or your future self. The `description` is the primary cognitive payload — its narrative is what lets another agent resume the work. The server also runs hybrid search on the description and attaches the most relevant memories to the checkpoint. Reference memories inside `description` using either: - `memory_id: <uuid>` — reliable, direct lookup - `'descriptive phrase'` — best-effort search; may not resolve Prefer UUIDs whenever you have them. The response reports `references_resolved` + `unresolved_references` so you can retry. For the full hygiene guide (what to include, how to organize, when to checkpoint, example shapes), invoke the `checkpoint_protocol` MCP prompt. Args: name: Unique identifier for this checkpoint (used by restore_context). description: Narrative handoff with optional memory references. ctx: MCP context (automatically provided). Returns: Dict with success status, context_id, memories_included, and (when references were extracted) references_resolved + unresolved_references.
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  • Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.
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