"Understanding local computer vision models like YOLO" matching MCP tools:

• get_available_modelsA

  Gonka Second Opinion (multi-model)A

  List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.

  ConnectorNo auth

• bbox_convertA

  Moltline Vision MathsA

  Convert bounding boxes between COCO, Pascal VOC and YOLO. FREE. The three formats disagree on everything: COCO is [x, y, width, height], VOC is [x1, y1, x2, y2], YOLO is [cx, cy, w, h] normalised to the image. Getting this wrong produces boxes that look plausible and quietly ruin every metric. Typical input {"boxes": [[10, 20, 100, 50]], "from_format": "coco", "to_format": "yolo", "image_width": 640, "image_height": 480} returns {"boxes": [[0.0938, 0.0938, 0.1562, 0.1042]], "converted": 1, "rejected": []}. Use whenever a dataset and a model disagree about format. Not for scoring predictions (detection_metrics) and not for removing overlaps (nms). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "boxes must contain at least one box"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• list_modelsA

  BridgeNode MCPA

  List available models and their prices — free, no payment, no authentication required. Read-only: no state changes; data is served from the server's local config, so repeated calls return identical results (idempotent). Accepts no parameters: the input schema is an empty object, and any arguments passed are ignored. Calling it without arguments returns the complete catalog with per-token prices; there is no filtering, pagination, or configuration. Use this tool to inspect models and prices before calling the paid chat_completions tool. Same data as GET /v1/models (§5.2). Do not use it to generate text (use chat_completions) or to estimate a specific request's cost (use get_price_estimate).

  ConnectorNo auth

• glyph_searchA

  FillinA

  Same as fillin_query, but returns the result pieces rendered as **photo glyph** image(s) — dense, vision-readable pages — followed by a JSON citation index ({n, source, url, title, published_at, page}). Read the image(s) directly with your vision capability; use the citation index to attribute or follow up. Glyphs are for comprehension and fact-extraction, not verbatim quotes (vision models paraphrase) — open the url for exact text. Billed at the flat /query rate; rendering is free.

  ConnectorNo auth

• get_region_signalA

  Horizon AI IntelligenceA

  The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.

  ConnectorNo auth

• aidefense_locate_conceptA

  website-searchA

  Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

  ConnectorNo auth

• reclassify_brand_assetsA

  HeistaA

  Batch re-run the vision tagger against every asset in a brand that hasn't been reviewed yet (vision_classified=false). Recovers rows the scan-time tagger dropped because of CDN blocks (Shopify hotlink, Cloudflare bot gates) or transient failures. Skips videos and rows already marked not_asset. Processes up to 24 assets per call — if more remain, the response returns { remaining > 0 } and the caller can invoke again. Paid (batched vision tag credit, typically < $0.01 per invocation).

  ConnectorNo auth

• openmeteo_get_climateA

  open-meteo-mcp-serverA

  Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (the observed archive, what happened). Daily resolution only. Available models: CMCC_CM2_VHR4, FGOALS_f3_H, HiRAM_SIT_HR, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S. A model name outside that list is sent upstream rather than rejected here, so a model Open-Meteo adds later still works; if upstream rejects the request, the error names the offending model on its own rather than the whole requested list. With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled they return a bounded preview instead.

  ConnectorNo auth

• compute.modelsA

  DPX — Institutional Cross-Border SettlementB

  List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.

  ConnectorNo auth

• list_tagsA

  UwearA

  List available tags for the user's company. Use this to resolve user-provided tag names like 'summer26' before selecting garments, outfits, models, files, generations, or locations. For no-UI MCP flows, find the tag ID here, then call get_items_by_tag or the relevant list_* tool with tag_ids.

  ConnectorNo auth

• compute.modelsA

  DPX — Institutional Cross-Border SettlementB

  List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.

  ConnectorNo auth

• get_product_visionA

  KeelenA

  Read a project's current product vision (what the product is for). READ THIS BEFORE YOU CALL `set_product_vision`: the setter REPLACES the whole document rather than appending to it, so writing without reading first silently discards whatever the user already recorded. To add a line, read the current text, edit it, and set the full result back. Returns {project_id, product_vision_md, updated_at}. `product_vision_md` is None when no vision has been set. Tenant-scoped: a project not in the caller's workspace 404s.

  ConnectorNo auth

• list_modelsA

  mcpA

  List the AI image, video, music, and sound-effect models available on BudgetPixel with base credit prices and capabilities. Featured models come first with a one-line role hint (when to pick each). Video models are priced per SECOND by resolution; music models are flat per track; sound effects are per second with a 3-second minimum. Prices are base rates — the user's plan discounts and free-model perks apply automatically when generating.

  ConnectorNo auth

• create_checkpointA

  Idle9A

  Save a checkpoint of this computer — the state of the filesystem and everything installed, kept under a name so you can put the machine back to exactly this state later. The computer keeps running; nothing is interrupted. Take one before anything you would struggle to undo: a database migration, a dependency or framework upgrade, a large refactor, a destructive script, or handing the machine to a different task. This is the persistent-computer version of "let me start fresh" — you cannot start fresh here, but you can come back. Only the first checkpoint copies the whole computer; each one after it saves what changed since the last, so taking one before every risky step is cheap and is the intended habit. There is still a limit on how many are kept, so name them for what they are ("before the schema change") rather than by number. The state is taken from the live filesystem, so finish or stop anything mid-write first — a database being written to, a build in progress — or that file is captured half-written.

  ConnectorOAuth

• check_deploy_prerequisitesA

  mcpA

  The deploy ENTRY GATE — run this first when a user wants to deploy. Verifies the account is ready and AUTO-SELECTS the network_id + keypair_name to pass to deploy_app (so you never hunt for them): (1) account reachable + quota, (2) a private network (picks your default — every account has one), (3) an SSH keypair (if you have none, returns ssh_key_recipe: the exact local ssh-keygen → import_keypair → write SSH_KEY_LOCATION-to-.env steps — run them, in yolo mode without asking). Returns { ready, network_id, keypair_name, missing, ssh_key_recipe }.

  ConnectorNo auth

• model_profilesA

  PlanExeA

  Optional helper before plan_create. Returns model_profile options with plain-language guidance and currently available models in each profile. If no models are available, returns error code MODEL_PROFILES_UNAVAILABLE.

  ConnectorNo auth

• computer_use.payA

  DPX — Institutional Cross-Border SettlementB

  Complete a payment that Claude's computer use session has identified on screen — a checkout form, wire transfer UI, invoice approval, or vendor portal payment step. Call this instead of typing credentials into a UI. Describe what you see on screen, provide the amount and recipient, and DPX runs the full oracle gate → compliance screen → settlement flow. Returns a receipt. Use whenever computer use encounters a payment that would otherwise require human re-entry or approval.

  Connector

  Destructive

  No auth

• get_available_filtersA

  FINN Auto - Car SubscriptionA

  Discover what's currently available in FINN's fleet. Returns all brands (with nested models), car types, fuel types, colors, subscription terms, gearshifts, and price/power/range bounds. Use this to answer questions like 'What brands does FINN offer?' or to validate filter values before searching.

  ConnectorNo auth

• trace_from_imageA

  kernelcadA

  Use this when you need to trace features from a reference photo into waypoints. Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.

  ConnectorNo auth

• get_datetimeA

  local-mcpA

  Get the current date and time of the machine where LMCP runs — with timezone and UTC offset. Call this whenever you need the real 'now' on the user's computer: before creating calendar events or reminders, resolving relative dates like 'today'/'tomorrow'/'next Friday', or timestamping. Takes no arguments.

  ConnectorOAuth

• cheapest_modelsA

  mcpA

  Find the cheapest current models, ranked by input price, output price, or a blended cost. The generic ranking covers generative text models (embeddings, OCR and realtime models are excluded — they price different work); pass category to rank a specific pool instead, e.g. 'embedding'. Use to answer 'what is the cheapest model for <use case>'.

  ConnectorNo auth