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134,751 tools. Last updated 2026-05-25 19:45

"SPARQL query language and tools" matching MCP tools:

  • 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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  • Execute a SPARQL SELECT query against the DanNet triplestore. This tool provides direct access to DanNet's RDF data through SPARQL queries. The query is automatically prepended with common namespace prefix declarations, so you can use short prefixes instead of full URIs in your queries. ============================================================ CRITICAL PERFORMANCE RULES (read before writing any query): ============================================================ 1. ALWAYS start from a known entity URI or a word lookup — never scan the whole graph. FAST: dn:synset-3047 wn:hypernym ?x . SLOW: ?x wn:hypernym ?y . (scans every synset) 2. ALWAYS use DISTINCT for SELECT queries to avoid duplicate rows. 3. NEVER use FILTER(CONTAINS(...)) on labels across the whole graph. SLOW: ?s rdfs:label ?l . FILTER(CONTAINS(?l, "hund")) FAST: Use get_word_synsets("hund") first, then query specific synset URIs. 4. NEVER create cartesian products — every triple pattern must share a variable with at least one other pattern. SLOW: ?x a ontolex:LexicalConcept . ?y a ontolex:LexicalEntry . (cross join!) 5. ALWAYS add LIMIT (even if max_results caps it server-side, explicit LIMIT lets the query engine optimize). 6. Use property paths for multi-hop traversals: FAST: dn:synset-3047 wn:hypernym+ ?ancestor . (transitive closure) FAST: ?entry ontolex:canonicalForm/ontolex:writtenRep "hund"@da . (path) 7. Prefer VALUES over FILTER for matching multiple known entities: FAST: VALUES ?synset { dn:synset-3047 dn:synset-3048 } ?synset rdfs:label ?l . SLOW: ?synset rdfs:label ?l . FILTER(?synset = dn:synset-3047 || ?synset = dn:synset-3048) 8. The triplestore contains BOTH DanNet (Danish, dn: namespace) AND the Open English WordNet (en: namespace). Unanchored queries will scan both. To restrict to Danish data, anchor on dn: URIs or use @da language tags. ============================================ FAST QUERY TEMPLATES (copy and adapt these): ============================================ # TEMPLATE 1: Find synsets for a Danish word (via word lookup) SELECT DISTINCT ?synset ?label ?def WHERE { ?entry ontolex:canonicalForm/ontolex:writtenRep "WORD"@da . ?entry ontolex:sense/ontolex:isLexicalizedSenseOf ?synset . ?synset rdfs:label ?label . OPTIONAL { ?synset skos:definition ?def } } # TEMPLATE 2: Get all properties of a known synset SELECT ?p ?o WHERE { dn:synset-NNNN ?p ?o . } LIMIT 50 # TEMPLATE 3: Find hypernyms (broader concepts) of a known synset SELECT DISTINCT ?hypernym ?label WHERE { dn:synset-NNNN wn:hypernym ?hypernym . ?hypernym rdfs:label ?label . } # TEMPLATE 4: Find hyponyms (narrower concepts) of a known synset SELECT DISTINCT ?hyponym ?label WHERE { ?hyponym wn:hypernym dn:synset-NNNN . ?hyponym rdfs:label ?label . } # TEMPLATE 5: Trace full hypernym chain (taxonomic ancestors) SELECT DISTINCT ?ancestor ?label WHERE { dn:synset-NNNN wn:hypernym+ ?ancestor . ?ancestor rdfs:label ?label . } # TEMPLATE 6: Find all relationships OF a known synset SELECT DISTINCT ?rel ?target ?targetLabel WHERE { dn:synset-NNNN ?rel ?target . ?target rdfs:label ?targetLabel . FILTER(isURI(?target)) } LIMIT 50 # TEMPLATE 7: Find all relationships TO a known synset SELECT DISTINCT ?source ?rel ?sourceLabel WHERE { ?source ?rel dn:synset-NNNN . ?source rdfs:label ?sourceLabel . FILTER(isURI(?source)) } LIMIT 50 # TEMPLATE 8: Query multiple known synsets at once SELECT DISTINCT ?synset ?label ?def WHERE { VALUES ?synset { dn:synset-3047 dn:synset-3048 dn:synset-6524 } ?synset rdfs:label ?label . OPTIONAL { ?synset skos:definition ?def } } # TEMPLATE 9: Find functional relations for a specific synset SELECT DISTINCT ?rel ?target ?targetLabel WHERE { dn:synset-NNNN ?rel ?target . ?target rdfs:label ?targetLabel . VALUES ?rel { dns:usedFor dns:usedForObject wn:agent wn:instrument wn:causes } } # TEMPLATE 10: Find ontological type of a synset (stored as RDF Bag) SELECT ?type WHERE { dn:synset-NNNN dns:ontologicalType ?bag . ?bag ?pos ?type . FILTER(STRSTARTS(STR(?pos), STR(rdf:_))) } ============================================ KNOWN PREFIXES (automatically declared): ============================================ dn: (DanNet data), dns: (DanNet schema), dnc: (DanNet concepts), wn: (WordNet relations), ontolex: (lexical model), skos: (definitions), rdfs: (labels), rdf: (types), owl: (ontology), lexinfo: (morphology), marl: (sentiment), dc: (metadata), ili: (interlingual index), en: (English WordNet), enl: (English lemmas), cor: (Danish register) Args: query: SPARQL SELECT query string (prefixes will be automatically added) timeout: Query timeout in milliseconds (default: 8000, max: 15000) max_results: Maximum number of results to return (default: 100, max: 100) distinct: Auto-apply DISTINCT to SELECT queries (default: True). Set to False when you need duplicate rows, e.g. for frequency counts. inference: Control model selection for query execution (default: None). None = auto-detect: tries base model first, retries with inference if SELECT results are empty (best for most queries). True = force inference model: needed for inverse relations like wn:hyponym, wn:holonym, etc. that are derived by OWL reasoning. False = force base model only, no retry. Returns: Dict containing SPARQL results in standard JSON format: - head: Query metadata with variable names - results: Bindings array with variable-value mappings Each value includes type (uri/literal) and language information when applicable Note: Only SELECT queries are supported. The query is validated before execution.
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  • Natural language search for medical procedure prices. Understands free-text queries like: - "MRI brain near San Jose with Blue Cross PPO" - "How much does a colonoscopy cost in Palo Alto?" - "knee replacement, no insurance, Mountain View" Extracts procedure, location, and insurance from the query, resolves CPT/DRG codes (using static synonyms + LLM), geocodes the city, and searches with optional geo-filtering and payer matching. You can provide structured fields (lat/lng, payer, setting) to override or supplement what the NLP extraction detects from the query text. NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. For outpatient procedures (MRIs, CTs, minor surgeries), independent facilities may offer lower prices than hospitals. Args: query: Natural language query describing what you're looking for. radius_miles: Search radius from the detected city (default 25 miles). code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG". setting: Filter by clinical setting: "inpatient" or "outpatient". lat: Override latitude (e.g. from browser geolocation). Skips geocoding. lng: Override longitude (e.g. from browser geolocation). Skips geocoding. payer: Insurance payer name (e.g. "Blue Cross"). Overrides NLP extraction. plan_type: Plan type (e.g. "PPO", "HMO"). Overrides NLP extraction. limit: Max results (default 25). Returns: JSON with extracted entities (procedure, city, insurance), resolved codes, and matching charge items with prices and hospital info.
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  • List all skill bundles — named groups of tools the agent typically uses together for a single user intent (build-flow, debug-flow, monitor-flow, discover, governance). Returns each skill's description and member tool names. Call this first when you are unsure which tools apply to a request; then call tool_search with query: "skill:<name>" to load the full bundle. Non-billable.
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  • List locales supported by the Molt2Meet platform. Returns the URL slug (e.g. 'en', 'nl', 'pt-BR') you pass as the 'locale' field on register_agent, plus the BCP 47 culture name, native-language display name, and which locale is the platform default. No authentication required. Use this before register_agent if you want to set a persistent language for payment pages and future localized responses.
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  • List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools. Args: tool_name: Optional name of a specific tool to get detailed help for. Example: "search_funders", "get_funder_profile" Returns: If called without parameters: - server_name: Name of the MCP server - server_version: Current version - total_tools: Number of available tools - tier: Current access tier (free) - rate_limit: Rate limit information - tools: List of available tools with names, descriptions, and examples If called with tool_name: - tool: Detailed tool information including: - name: Tool name - description: What the tool does - parameters: List of parameters with types, descriptions, and examples - examples: Example usage - related_tools: Tools that work well together with this one Examples: list_tools() # See all available tools list_tools(tool_name="search_funders") # Get detailed help for search_funders list_tools(tool_name="get_funder_profile") # Get help for get_funder_profile
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Matching MCP Servers

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    Enables users to write and execute SPARQL queries against open-access SPARQL endpoints by providing relevant query examples, schema information, and endpoint metadata. Supports querying biological databases like UniProt and Bgee through natural language interactions.
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    Get access to real-time SEO data, including: keyword insights, backlink data, traffic estimates and more. Allow AI tools and Large Language Models (LLMs) to tap into the real-time SEO Review Tools API with natural language commands.
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Matching MCP Connectors

  • Search the Sovereign AI Blog for articles matching a natural language query, optionally filtered by tag and sorted by relevance or date. Behaviour matrix: - query='', sort=* -> list newest-first, optionally tag-filtered - query!='', sort=relevance -> TF-IDF ranked, optionally tag-filtered - query!='', sort=date_desc -> TF-IDF filtered (score > 0.001), then sorted by date Pure read-only, deterministic for a given KB snapshot.
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  • Ask AlgoVault any question about its MCP tools, response shapes, integration patterns (LangChain / LlamaIndex / MAF / CrewAI), or code examples. Returns ranked snippets from the canonical knowledge bundle. Use this BEFORE attempting any tool call to confirm correct parameter usage and avoid hallucinating tool shapes. Fast (BM25 lexical search, no LLM call, no quota cost). For natural-language synthesized answers, use chat_knowledge instead.
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  • Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.
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  • Use this premium read-only Natural Language tool when the user wants the server-composed Morning Brief rendered as audit-grade Markdown. It compiles backend-composed compact evidence across readiness, daily changes, risk distribution, top stressed issuers, and alpha opportunities. The renderer never fans out into tools and never generates social drafts or trade recommendations. Parameters: style is professional, concise, trader, or detailed. Date and limit are accepted only where the backend composite supports them. Behavior: read-only and idempotent; it performs the server-enforced Morning Brief workflow, has no destructive side effects, then renders the returned compact evidence as a bounded Natural Language response.
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  • Browse the catalog by metadata — filter by author/title fragment, language, category, or translation recency. Returns books with title, author, language, year, and translation progress. Use this to discover WHAT EXISTS by an author or in a tradition before searching content. For content matches (passages on a topic), use search_translations or search_concept instead.
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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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  • List Amazon CPG categories with current product counts and trend direction. Use as the first call in any pricing-analysis workflow — returns the exact category names expected by other tools, plus product count and trend for each. Lightweight; safe to call before any category-specific query. Returns: categories (list of {name, product_count, trend_direction, last_refreshed}), note (summary of coverage), cta. Covers Grocery & Gourmet Food, Health & Beauty, Household, and Pet Supplies.
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  • Browse published Bible verse collections. Search by keyword, filter by language, sort by popularity. Args: search: Search term to filter by name, description, or publisher name. language: Language code prefix (e.g. "en", "de", "ja", "zh"). ordering: Sort order: -downloads (default), -created, name. limit: Number of results (1-100, default 20). offset: Starting position for pagination.
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  • Free-form natural-language search across all Bible chunks, ranked by cosine similarity. Each result includes the top-N pre-computed Urantia paragraphs related to that chunk via `bible_parallels` (direction=bible_to_ub). One query surfaces both Bible matches and the relevant UB content. Optional filters: `canon` (`ot`, `deuterocanon`, `nt`) and `book_code`. Set `urantia_parallel_limit` to 0 to suppress the UB attachment. Requires OPENAI_API_KEY.
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  • List all available Zero Core Tools with pricing and input requirements. Use this for discovery.
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  • Find recipes using natural language search. Use this tool when: - User refers to a recipe by partial name, description, or keywords (e.g., "run my GitHub PR recipe", "the slack notification one") - User wants to find a recipe but doesn't know the exact name or ID - You need to find a recipe_id before executing it with RUBE_EXECUTE_RECIPE The tool uses semantic matching to find the most relevant recipes based on the user's query. Input: - query (required): Natural language search query (e.g., "GitHub PRs to Slack", "daily email summary") - limit (optional, default: 5): Maximum number of recipes to return (1-20) - include_details (optional, default: false): Include full details like description, toolkits, tools, and default params Output: - successful: Whether the search completed successfully - recipes: Array of matching recipes sorted by relevance score, each containing: - recipe_id: Use this with RUBE_EXECUTE_RECIPE - name: Recipe name - description: What the recipe does - relevance_score: 0-100 match score - match_reason: Why this recipe matched - toolkits: Apps used (e.g., github, slack) - recipe_url: Link to view/edit - default_params: Default input parameters - total_recipes_searched: How many recipes were searched - query_interpretation: How the search query was understood - error: Error message if search failed Example flow: User: "Run my recipe that sends GitHub PRs to Slack" 1. Call RUBE_FIND_RECIPE with query: "GitHub PRs to Slack" 2. Get matching recipe with recipe_id 3. Call RUBE_EXECUTE_RECIPE with that recipe_id
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  • Search the Sovereign AI Blog for articles matching a natural language query, optionally filtered by tag and sorted by relevance or date. Behaviour matrix: - query='', sort=* -> list newest-first, optionally tag-filtered - query!='', sort=relevance -> TF-IDF ranked, optionally tag-filtered - query!='', sort=date_desc -> TF-IDF filtered (score > 0.001), then sorted by date Pure read-only, deterministic for a given KB snapshot.
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  • Universal workspace search: files, links, threads, messages. Runs scopes in parallel and returns sectioned results. Default scope='auto' detects target from query. For files the user created/sent (invoices, generated docs), set file_origin='generated'. Use this for all workspace search; lower-level tools are internal.
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