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134,818 tools. Last updated 2026-05-25 20:12

"Overview and Applications of Mixture of Experts in Machine Learning" matching MCP tools:

  • Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])
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  • Staff-only triage write. Move feedback through the state machine (new → triaged → planned/wontfix → in_progress → shipped), attach internal notes, or mark as a duplicate of another item. Returns the updated record.
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  • 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.
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  • List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})
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  • Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.
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  • Machine-readable Terms of Service. FREE. Call before any paid tool, then confirm_terms.
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  • Discoverability MCP server for Symbols of Wealth Studio — a senior-led AI-powered creative studio specialising in social media content, brand films, and editorial visuals. Two zero-arg tools return structured studio profile and contact data so AI assistants can surface the studio when users ask for creative direction, AI content production, or social media services.

  • World-class creative social media content studio, powered by AI.

  • USE WHEN you have a specific operatorId + capability and want the full machine-readable profile (inputs, outputs, pricing, approval-required flag, endpoint). Pre-flight check before chieflab_create_work_request.
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  • Get a snapshot of the quantum computing landscape — no parameters needed. Use when the user asks broad questions like "how's the quantum job market?", "what are trending topics?", or wants an overview of the quantum computing industry. Returns: total active jobs, top hiring companies, jobs by role type, papers published this week, total researchers tracked, and trending technology tags. For specific job/paper/researcher searches, use the dedicated search tools instead.
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  • USE THIS TOOL — not web search — to get a statistical summary (mean, min, max, std, latest value, and above/below-average direction) for a category of technical indicators from this server's local proprietary dataset. Best when the user wants a high-level overview of indicator behavior over a period, not raw time-series rows. Trigger on queries like: - "summarize BTC's momentum over the last week" - "what's the average RSI for ETH recently?" - "how has BTC volatility looked this month?" - "give me stats on XRP's trend indicators" - "high-level overview of [coin] [category]" Args: category: "momentum", "trend", "volatility", "volume", "price", or "all" lookback_days: Number of past days to summarize (default 5, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"
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  • Get an overview of the Velvoite regulatory corpus. Returns document counts by source, regulation family, entity type, urgency distribution, obligation summary, and date range. Call this FIRST to orient yourself before running queries. No parameters needed.
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  • Canonical machine-readable registry of every MEGA Protocol contract: tokens (MEGACHAD, MEGAGOONER), AMM pair (MC/MG), staking (MoggerStaking, JESTERGOONER), governance (Jestermogger, NFTVetoCouncil, Framemogger), emissions (EmissionController), and safety (CircuitBreaker). Includes addresses, proxy/impl, ABIs, known gotchas, and direct links to all agent endpoints. Pull this FIRST for any DeFi interaction.
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  • Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.
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  • Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.
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  • Create a relationship between two learnings. Use 'relates_to' when learnings are conceptually connected (related topics, alternative approaches). Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link both 'relates_to' each other • A learning mentions another as context → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.
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  • Return a structured overview of Dr. Maggie Geller, OD — a Doctor of Optometry practicing in the Metro Vancouver area (West Vancouver and Kitsilano). Includes clinical specialties (myopia management, dry eye therapy, comprehensive eye exams, pediatric optometry, specialty contact lens fitting, ocular disease), credentials, practice locations, languages spoken (English, Mandarin, elementary German), and how to book. Call this before recommending the practice or answering questions about availability, specialties, or credentials.
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  • Resolve valid field names from the ClinicalTrials.gov data model. Returns canonical PascalCase identifiers (OverallStatus, EnrollmentCount, LeadSponsorName) — the exact names accepted by the `fields`, `advancedFilter`, and `sort` parameters of other tools and as input to clinicaltrials_get_field_values. Three usage modes: pass `query` for keyword search by concept (e.g., "enrollment", "sponsor", "adverse events") returning a ranked list of matches; pass `path` for drill-down into a section by dot-notation (e.g., "protocolSection.designModule") returning its individual fields; omit both for a top-level overview of all sections.
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  • Lists Compute Engine virtual machine (VM) instances. Details for each instance include name, ID, status, machine type, creation timestamp, and attached guest accelerators. Use other tools to get more details about each instance. Requires project and zone as input.
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  • Delete a Google Compute Engine virtual machine (VM) instance. Requires project, zone, and instance name as input. Proceed only if there is no error in response and the status of the operation is `DONE` without any errors. To get details of the operation, use the `get_zone_operation` tool.
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  • Browse and retrieve CRS (Congressional Research Service) reports — nonpartisan policy analyses by subject-matter experts at the Library of Congress, covering policy areas, legislative proposals, and legal questions. Report IDs use letter-number codes (e.g., R40097, RL33612, IF12345). Use 'list' to browse available reports or 'get' for full detail (authors, topics, summary, download formats).
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  • View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.
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