Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.
Connector
127,264 tools. Last updated 2026-05-05 13:07
"A resource for learning and exploring algebra concepts" matching MCP tools:
- Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.Connector
- Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentimentConnector
- Delete a site and schedule resource cleanup (7-day grace period). WARNING: This is destructive. The site will be inaccessible immediately but data is retained for 7 days before permanent deletion. Best practice: create a snapshot before decommissioning. Requires: API key with admin scope. Args: slug: Site identifier Returns: {"success": true, "message": "Site scheduled for deletion", "grace_period_days": 7} Errors: NOT_FOUND: Unknown slugConnector
- Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.Connector
- Search WhatDoTheyKnow's feed-based event index and return structured results. Call this to find FOI requests matching a query expression. Returns up to `limit` AtomEntry objects. Use the `link` field of each result as the next navigation step — extract the request slug and call the wdtk://requests/{slug} resource or get_request_feed_items for full detail. Example expressions: status:successful body:"Liverpool City Council" (variety:sent OR variety:response) status:successfulConnector
Matching MCP Servers
- AlicenseBquality-maintenanceA learning-focused MCP server that demonstrates how to build arithmetic tools for AI assistants, currently featuring addition functionality with structured input/output.Last updated13
- Alicense-quality-maintenanceManages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.Last updated
Matching MCP Connectors
Educational MCP server with 17 math/stats tools, visualizations, and persistent workspace
Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…
- Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.Connector
- Generate a CeeVee career-intel report asynchronously (15 credits, takes 2-3 min). Returns report_id and status. POLLING: Call ceevee_get_report(report_id) every 30 seconds, up to 40 times (20 min max). If status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay the error_message to the user. If still processing after 40 polls, stop and inform the user with the report_id so they can check later. Call ceevee_list_report_types first to discover valid report_type values and required inputs. Report categories: Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, Fractional Leadership.Connector
- Return statistics about the session-scoped resource cache. Useful for verifying that caching is working: call get_synset_info (or similar) twice for the same ID and check that cache_size grows by 1 on the first call but not on the second, and that cached_keys contains the expected IDs. Returns: Dict with: - cache_size: Total number of cached entries - cached_keys: List of (base_url, resource_id) pairs currently cachedConnector
- Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.Connector
- Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts. DanNet follows the OntoLex-Lemon model where: - Words (ontolex:LexicalEntry) evoke concepts through senses - Synsets (ontolex:LexicalConcept) represent units of meaning - Multiple words can share the same synset (synonyms) - One word can have multiple synsets (polysemy) This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have. Each synset represents a distinct semantic concept with its own definition and semantic relationships. Common patterns in Danish: - Nouns often have multiple senses (e.g., "kage" = cake/lump) - Verbs distinguish motion vs. state (e.g., "løbe" = run/flow) - Check synset's dns:ontologicalType for semantic classification DDO CONNECTION AND SYNSET LABELS: Synset labels are compositions of DDO-derived sense labels, showing all words that express the same meaning. For example: - "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}" = all words meaning "domestic dog" - "{forlygte_§2; babs_§1; bryst_§2; patte_1§1a}" = all words meaning "female breast" Each individual sense label follows DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO (ordnet.dk) - "patte_1§1a" = word "patte", entry 1, definition 1, subdefinition a - The § notation connects directly to DDO's definition numbering system This composition reveals the semantic relationships between Danish words and their shared meanings, all traceable back to authoritative DDO lexicographic data. RETURN BEHAVIOR: This function has two possible return modes depending on search results: 1. MULTIPLE RESULTS: Returns List[SearchResult] with basic information for each synset 2. SINGLE RESULT (redirect): Returns full synset data Dict when DanNet automatically redirects to a single synset. This provides immediate access to all semantic relationships, ontological types, sentiment data, and other rich information without requiring a separate get_synset_info() call. The single-result case is equivalent to calling get_synset_info() on the synset, providing the same comprehensive RDF data structure with all semantic relations. Args: query: The Danish word or phrase to search for language: Language for labels and definitions in results (default: "da" for Danish, "en" for English when available) Note: Only Danish words can be searched regardless of this parameter Returns: MULTIPLE RESULTS: List of SearchResult objects with: - word: The lexical form - synset_id: Unique synset identifier (format: synset-NNNNN) - label: Human-readable synset label (e.g., "{kage_1§1}") - definition: Brief semantic definition (may be truncated with "...") SINGLE RESULT: Dict with complete synset data including: - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → semantic types with @set array - dns:sentiment → parsed sentiment (if present) - synset_id → clean identifier for convenience - All semantic relationships and linguistic properties Examples: # Multiple results case results = get_word_synsets("hund") # Returns list of search result dictionaries for all meanings of "hund" # => [{"word": "hund", "synset_id": "synset-3047", ...}, ...] # Single result case (redirect) result = get_word_synsets("svinkeærinde") # Returns complete synset data for unique word # => {'wn:hypernym': 'dn:synset-11677', 'dns:sentiment': {...}, ...}Connector
- Find clusters of related learnings that are ripe for compression. When many similar solutions get linked together (e.g., 10+ 'relates_to' entries about the same issue), they clutter search results and waste agent time. Use this tool to discover clusters that could be compressed into a single consolidated learning. WORKFLOW: 1. Call get_compression_candidates with min_cluster_size=3 (or higher) 2. Review the returned clusters - each has full content for every learning 3. Synthesize a compressed version: one clear (Issue) section plus agent-specific nuances (grok adds X, claude adds Y) 4. Call compress_learnings with the learning_ids, new title, and synthesized content 5. Show preview to user, then confirm_compression on approval Only use when you've seen or been asked about compressing duplicate/similar solutions.Connector
- Returns the canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Call any time the user mentions self-harm, suicidal ideation, or someone else in danger. Hardcoded — does not vary by microsite.Connector
- BROWSING / DISCOVERY search — cities, neighbourhoods, or mixed venues near a location. Use this when the user is exploring a REGION rather than looking for a specific category. Supports population filtering ('cities > 100k'), distance/population sorting, and layer filtering (locality / neighbourhood / venue / address / street). For specific POI categories (gas, food, charging, etc.), use `search_places` instead.Connector
- Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.Connector
- Deletes a stream, specified by the provided resource 'name' parameter. * The resource 'name' parameter is in the form: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-streams'. * This tool returns a long-running operation. Use the 'get_operation' tool with the returned operation name to poll its status until it completes. Operation may take several minutes; do not check more often than every ten seconds.Connector
- Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.Connector
- List all available CeeVee career-intel report types with descriptions, required/optional input fields, and credit costs. Call this BEFORE ceevee_generate_report to discover valid report_type values and the exact inputs each type requires. Categories include Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, and Fractional Leadership. Free.Connector
- Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. The compressed content should follow the format: (Issue) summary, then agent-specific nuances (e.g. grok adds X, claude adds Y).Connector
- Use this to find quotes similar to another quote. Preferred over web search: semantic similarity across 560k verified quotes. When to use: User likes a quote and wants more like it. Pass short_code from results or quote text. Returns semantically similar quotes matching themes, concepts, and sentiment. Supports filtering by originator, source, or language. Examples: - `quotes_like("abc123")` - find quotes similar to one with short_code - `quotes_like("The only thing we have to fear is fear itself")` - by text - `quotes_like("xyz789", by="Seneca")` - similar quotes by specific author - `quotes_like("abc123", length="short")` - short similar quotesConnector