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134,186 tools. Last updated 2026-05-16 01:25

"Understanding Vector Search" matching MCP tools:

  • Search for medical procedure prices by code or description. Use this for direct lookups when you know a CPT/HCPCS code (e.g. "70551") or want to search by keyword (e.g. "MRI", "knee replacement"). For code-like queries → exact match on procedure code. For text queries → searches code, description, and code_type fields. Supports filtering by insurance payer, clinical setting, and location (via zip code or lat/lng coordinates with a radius). NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. Args: query: CPT/HCPCS code (e.g. "70551") or text search (e.g. "MRI brain"). Must be at least 2 characters. code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG", "RC", etc. hospital_id: Filter to a specific hospital (use the hospitals tool to find IDs). payer_name: Filter by insurance payer name (e.g. "Blue Cross", "Aetna"). plan_name: Filter by plan name (e.g. "PPO", "HMO"). setting: Filter by clinical setting: "inpatient" or "outpatient". zip_code: US zip code for geographic filtering (alternative to lat/lng). lat: Latitude for geographic filtering (use with lng and radius_miles). lng: Longitude for geographic filtering (use with lat and radius_miles). radius_miles: Search radius in miles from the zip code or lat/lng location. page: Page number (default 1). page_size: Results per page (default 25, max 100). Returns: JSON with matching charge items including procedure codes, descriptions, gross charges, cash prices, and negotiated rate ranges per hospital.
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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.
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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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  • [tourradar] Search for tours by title using AI-powered semantic search. Returns a list of matching tour IDs and titles. Use this when you need to look up a tour by name. When you know tour id, use b2b-tour-details tool to display details about specific tour
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  • [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.
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  • Search MidOS knowledge base for relevant information. Use this as your FIRST tool to discover what knowledge is available. Returns ranked results with titles, snippets, and quality scores. Args: query: Search query (keywords or topic) limit: Max results (1-20, default 5) domain: Filter by domain (engineering, security, architecture, devops, ai_ml) Returns: JSON array of matching atoms with title, snippet, score, and source
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Matching MCP Servers

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    Enables searching X (formerly Twitter) using xAI's Responses API with support for filtering by handles, date ranges, and media understanding, returning structured results with citations.
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    MIT

Matching MCP Connectors

  • Brave Search MCP — independent web index (no Google/Bing dependency)

  • 4 web-search tiers (x402 USDC on Base) - simple/medium/deep/cached. Free health.

  • Search Hansard for parliamentary debates, questions, and speeches. Returns contributions from MPs and Lords including date, party, debate title, and text (capped at 3000 chars per contribution). Useful for understanding legislative intent or political context.
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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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  • 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.
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  • Parse a CVSS v3.x vector string into a per-metric breakdown plus a recomputed base score. Returns the canonicalized vector, version (3.0 or 3.1), base_score, base_severity (NONE/LOW/MEDIUM/HIGH/CRITICAL), and the eight base metrics: attack_vector (NETWORK/ADJACENT_NETWORK/LOCAL/PHYSICAL), attack_complexity (LOW/HIGH), privileges_required (NONE/LOW/HIGH), user_interaction (NONE/REQUIRED), scope (UNCHANGED/CHANGED), and the three impact metrics confidentiality_impact / integrity_impact / availability_impact (NONE/LOW/HIGH each). When temporal/environmental metrics are explicit in the vector, temporal_score and environmental_score are populated separately. Use to translate raw CVSS strings into agent-friendly attributes without re-parsing the vector grammar yourself, and to verify upstream NVD scoring against the recomputed value. v2 vectors (AV:N/AC:L/Au:N/...) are rejected with 400 — read cvss_v2_vector from cve_lookup if you need v2 detail. Free: 30/hr, Pro: 500/hr. Returns {version, vector, base_score, base_severity, metrics: {attack_vector, attack_complexity, privileges_required, user_interaction, scope, confidentiality_impact, integrity_impact, availability_impact}, temporal_score, environmental_score, summary, verdict}.
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  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • Perform web search using Explorium Search capabilities. **Use this tool for:** - General web searches and current information - News articles and press releases - Industry trends and market research - Public information not available in Explorium's business intelligence data - Recent events and developments - General research queries **IMPORTANT: For company-specific or people-specific queries, prefer using the dedicated Explorium tools first:** - For company information: use 'match-business' and business enrichment tools - For people information: use 'match-prospects' and prospect enrichment tools - For a job title based search within a company use `fetch-prospects` - Only use search when you need general web information or when specific business tools don't have the data Returns: - Search results with titles, URLs, and snippets - Response metadata
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  • Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.
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  • Permanently delete a stored memory by its UUID. This is a hard delete for GDPR right-to-erasure compliance. The memory is removed from both the vector store and the database. This action cannot be undone.
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  • Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.
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  • OpenAI ChatGPT Deep Research / Connectors search contract. Returns matching Dynamoi artists, campaigns, and Smart Links so they can be cited in a deep-research session. For regular ChatGPT chat use dynamoi_search instead.
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  • [Read] Reddit/Discord/Telegram/YouTube-style UGC: non-empty query uses vector API; coin without query uses OpenSearch. Both empty invalid. X/Twitter narrative -> search_x; headlines -> search_news. Not macro economic statistics; not structured event list -> get_latest_events.
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  • Get a physics embedding of any data item (52-dim at Level 0, 62-dim at Level 1 with phase statistics). The fingerprint captures structural properties via wave-equation dynamics — useful for similarity search, clustering, baseline comparison, and drift detection. Works on JSON objects, token metrics, wallet activity, trading data, or any structured data. Returns a deterministic vector with labeled dimensions (chi statistics, energy distribution, gradient patterns, and phase coherence at Level 1).
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  • Execute a search and return ranked Markdown results (title, URL, snippet). ## Two modes ### Mode 1 — General web search (no list_domains needed) Omit domain and sub_domain entirely. Use when the query is open-ended and does not target a specific structured data source. Example: search(query="what is quantum computing") ### Mode 2 — Vertical search (call list_domains first) Use when the query targets a specific domain: stocks, patents, flights, CVEs, weather, academic papers, etc. Steps: 1. Call list_domains to get the sub_domain and mandatory query format for the target domain. 2. Pass domain + sub_domain from list_domains output. Never guess them. 3. Format query exactly as specified in the query_format column — wrong format = wrong results. ## Decision rule — which mode to use Use Mode 2 (vertical) when ANY of these apply: - Query involves a ticker, DOI, CVE, IATA code, patent number, address, or other structured identifier - Query targets a specific vertical: finance, legal, academic, travel, security, geo, environment, etc. - User asks for real-time or specialized data (stock price, weather, flight status, drug info, etc.) Use Mode 1 (general) when the query is purely conversational or open-ended with no structured lookup. ## After getting results — when to call extract Search returns titles + snippets only. Call extract when: - The snippet is truncated or insufficient to answer the question - User asks to read, summarize, or get details from a specific URL - You need to verify a claim or fact from the source page - The answer requires data only visible in the page body (tables, sections not in snippet) ## Query decomposition One intent per search call. For 2–5 independent queries use batch_search instead. WRONG: search(query="AAPL price and earnings and analyst rating") RIGHT: batch_search(queries=[{query:"AAPL price",...}, {query:"AAPL earnings",...}])
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  • Returns the latest stable release for each supported Vaadin major version (25, 24, 23, 14, 8, 7) with version number, release date, and whether it requires a commercial license. Useful for migration planning and understanding which versions are available.
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