"Assistance with Creating Plotly Graphs Using AI" matching MCP tools:

• create_b2_snapshot

  BorealHost

  Create a B2 cloud-backed snapshot (zero local disk, async). Streams container data directly to Backblaze B2 via restic. No local disk impact — billed separately at cost+5%. Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed". Only available for VPS plans. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "...", "status": "creating", "storage_type": "b2", "message": "B2 cloud snapshot started. Poll list_snapshots()..."} Errors: VALIDATION_ERROR: Not a VPS plan or max snapshots reached

  Connector

• create_snapshot

  BorealHost

  Create a local container snapshot (async). Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed" or "failed". Available for VPS, dedicated, and cloud plans (any plan with max_snapshots > 0). Local snapshots are stored on the host disk and count against disk quota. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "snap-...", "status": "creating", "storage_type": "local", "message": "Snapshot started. Poll list_snapshots() to check status."} Errors: VALIDATION_ERROR: Max snapshots reached or insufficient disk quota

  Connector

• chart_instructions

  Autario Data Analytics Platform

  Get the Builder spec schema reference. Returns chart_type enum, required/optional fields per type, palette options, axis-override shape, annotation format, and concrete examples. Call this ONCE at session-start; the spec it returns is the input shape for create_chart_from_spec. Cheaper and clearer than guessing Plotly JSON syntax.

  Connector

• ai_filters_list

  dialogbrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

  Connector

• get_api_key_instructions

  Kamy

  Return step-by-step instructions for creating a Kamy API key in the dashboard. Does not open the browser.

  Connector

• create_edit_session

  mcp

  Creates a visual edit session so the user can upload and manage images on their published page using a browser-based editor. Returns an edit URL to share with the user. When creating pages with images, use data-wpe-slot placeholder images instead of base64 — then create an edit session so the user can upload real images.

  Connector

• ai_tags_list

  dialogbrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

  Connector

• confirm_ai_call

  Sats4AI - Bitcoin-Powered AI Tools

  Confirm an AI call after reviewing push-back questions, optionally providing answers to missing info. Required when ai_call returns state='pending_confirm'. Uses the original payment — no new payment needed. Returns call_id for polling with check_job_status(jobType='ai-call').

  Connector

• get_guide

  Canvs

  ⚠️ MANDATORY FIRST STEP - Call this tool BEFORE using any other Canvs tools! Returns comprehensive instructions for creating whiteboards: tool selection strategy, iterative workflow, and examples. Following these instructions ensures correct diagrams.

  Connector

• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results. Two-step flow: first call `brave_web_search` with `summary=true` to obtain `summarizer.key`, then pass it here. Pro AI tier required.

  Connector

• ai_filters_list

  DialogBrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

  Connector

• get_graph_template_schemas

  RationalBloks

  Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.

  Connector

• ai_tags_list

  DialogBrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

  Connector

• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

  Connector

• get_template_schemas

  RationalBloks

  Get pre-built template schemas for common use cases. ⭐ USE THIS FIRST when creating a new project! Templates show the CORRECT schema format with: proper FLAT structure (no 'fields' nesting), every field has a 'type' property, foreign key relationships configured correctly, best practices for field naming and types. Available templates: E-commerce (products, orders, customers), Team collaboration (projects, tasks, users), General purpose templates. You can use these templates directly with create_project or modify them for your needs. TIP: Study these templates to understand the correct schema format before creating custom schemas.

  Connector

• order_create

  Kash.click

  Record a new order in the connected shop. Input includes paymentMode, and items[]. Each item can be of type 'catalog' (with productId), 'department' (with price and deptId) or 'free' (with title and price). Check if the client already exists using data_list_clients and if the client exists, only specify idClient. If provided, paymentMode should correspond to a payment ID from data_list_payments_modes tool. Returns a sale confirmation JSON including: a link to the PDF invoice, and a link to a private order page showing full order details which can also be used by the client to pay online. IMPORTANT: before creating a validated invoice (payment ≠ -2), call account_show_infos to verify that shopName, adressline1, and companyRegistrationNum are all set. If any of these fields are empty, warn the user and suggest using account_edit to fill them in before issuing invoices.

  Connector

• tourradar_vertex-review-search

  MolTravel

  [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.

  Connector

• ai_filters_test

  dialogbrain

  Test a message against an AI filter to check whether it would match. This tool embeds the provided message using Voyage AI and computes the cosine similarity between the message vector and the filter's stored reference vector. It returns the similarity score, whether the message would match (similarity >= threshold), and the filter's threshold value. Use this to: - Verify a filter works as intended before using it in a trigger - Tune the threshold by testing borderline messages - Debug why a message did or did not match a filter in production Returns: {similarity: float, matched: bool, threshold: float} Note: This tool calls the Voyage AI embedding API to embed the test message.

  Connector

• submit_support_request

  Skala — Legal Platform for Startups

  Submit a support request to the Skala team on behalf of the user. Call this when the user needs human assistance that AI cannot provide, the question is too complex or high-risk, or the user explicitly asks for human support. IMPORTANT: Always confirm with the user before calling — describe what you will submit and ask for their approval. Before calling, compile the issue from conversation context into the description.

  Connector

• scrutica_get_company

  Scrutica

  Fetch complete details for a single organization (company, government entity, holding company) by canonical Scrutica ID. Returns: legal name, country of HQ, organization type, parent / subsidiary references, supply-chain edge counts. Use scrutica_query_export_controls for BIS designation details. Use scrutica_get_supply_chain for full edge graphs.

  Connector