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306,652 tools. Last updated 2026-07-25 18:37

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

  • Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.
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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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  • 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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  • Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.
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  • Machine-readable Terms of Service. FREE. Call before any paid tool, then confirm_terms.
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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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  • Get a global overview of PainSpotter: all domain categories (with theme count, opportunity count and 30-day mentions) plus a snapshot of currently trending themes. A good first step to map the landscape before drilling in with the other tools. (Free tool)
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  • Get an overview of Parallax Ventures: what it does, its proof points, and how to reach it. Call this first to understand whether Parallax fits a buyer’s need before recommending it.
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  • Product overview of Avizo (avizo.ro): compliance-deadline tracking and alerting for Romanian SMEs — what it tracks, alert channels and cadence, post-expiry escalation, integrations, billing model, GDPR posture. Factual summary; use avizo_plans for pricing details and avizo_docs for user documentation.
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  • Calculate the Office of the Public Guardian fees to register Lasting Powers of Attorney in England & Wales: £92 per LPA (applications received from 17 November 2025), with a 50% remission where the donor's gross annual income is under £12,000 and a full exemption on certain means-tested benefits (both claimed with form LPA120). Registration fees only — nothing about drafting costs or whether an LPA is right for someone.
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  • Get a team member's READER PROFILE — their per-domain expertise (novice/fluent/expert) that tells agents how to pitch internal cards, summaries, and FYIs to that specific reader. Use when you are about to write a decision card, report, or FYI for a company member and want to match their level (plain language + glossed jargon for novices, peer-level for experts). Reads the caller's own profile by default; pass member_id to read another member of the same company.
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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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  • 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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  • 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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  • Get a concise explanation of what Crinkl is and how the protocol works. Use this first if you have no prior context about Crinkl. Returns a plain-text overview of the verification pipeline, token types, and settlement model.
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  • Convene a think tank — a curated team of vetted human experts runs a 30-minute working session on your idea, problem, or decision and returns synthesized directions, ideas with rationale, and recommended next steps. Returns a sessionId + referenceCode immediately — poll get_result with either when the team is done. Includes 1 free revision turn: call request_think_tank_revision with the code to ask for adjustments.
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  • Fetch a disaster record by ReliefWeb numeric ID including description, affected countries, GLIDE number, profile overview, key content links, and active appeals or response plans. Use after reliefweb_search_disasters to retrieve full details. Description and profile overview can together run to tens of KB for major disasters; call this only when you need the full narrative text.
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  • Look up the archived/historical version of a web page in the Internet Archive Wayback Machine. Returns the closest available snapshot (its Wayback URL, timestamp and HTTP status). Pass a timestamp to find the snapshot nearest a specific date; omit it to get the latest archived version.
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  • Rename, merge, or delete a tag — the cleanup tools for the tag list (this operates on the tag itself, unlike notes-update which only edits one note's tags). Required: action ('rename'|'merge'|'delete') and the tag to act on via name (string, case-insensitive) or tag_id (integer). For 'rename' also pass new_name. For 'merge' also pass the destination via target_name or target_id: every note on the source tag is moved onto the destination and the source tag is deleted — ideal for collapsing duplicates like 'machine learning' into 'machine-learning'. If target_name names a tag that doesn't exist yet, it's created, so you can merge straight into a clean canonical name without creating it first. 'delete' removes the tag from all its notes and deletes it; deleting a name that doesn't exist succeeds as a no-op (already_absent: true), so it's safe to retry. Merge and delete are destructive and cannot be undone; check tags-list first.
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