"Interacting with AI models like OpenAI or Google models" matching MCP tools:

• get_available_models

  Gonka Second Opinion (multi-model)

  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.

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• model_oracle

  Zambo AI Tools

  Zambo Stack — Get a ranked recommendation of AI models for your specific task, token budget, and cost constraints. Covers Groq, Anthropic Claude, OpenAI, Google Gemini — 10 models tracked with current June 2026 pricing. Add use_case for a personalized Groq-powered insight. 30 free/day. Best for: 'which model should I use for summarization?', 'cheapest model for classification', 'compare GPT-4o vs Claude Sonnet for coding'.

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• model_info

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature list. Covers 30+ models from OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral, Cohere, xAI.

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• search_models

  mcp

  Search the live UnoRouter model catalog (200+ models behind one OpenAI-compatible key). Models ending in :free cost nothing. Returns matching model IDs usable with the chat tool or any OpenAI-compatible client pointed at https://api.unorouter.com/v1.

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• list_llm_models

  IA-QA — 130+ QA & Dev Tools for AI Agents

  List all LLM models available on ia-qa.com with their provider, API endpoint, and capabilities. Filter by provider name (e.g. "Groq", "HuggingFace", "OpenAI") or return the full catalog. Use this to discover which models are available before calling an LLM API, or to compare providers.

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• ai_chat

  MapleFlow

  Send a message to any of 30+ AI models (OpenAI, Anthropic, Google, Groq, xAI). Returns the model's response. Supports conversation history via the messages array.

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• thinkneo_route_model

  ThinkNEO Control Plane

  AI Smart Router — find the cheapest model that meets your quality threshold. Specify your task type and quality requirements, and ThinkNEO will recommend the optimal model with estimated cost and savings vs premium models. Supports 17+ models across Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Alibaba, Cohere, and xAI. Requires authentication.

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• list_models

  ScanBIM MCP

  List every object currently stored in the scanbim-models OSS bucket, with URN, size in MB, and a viewer URL for each. Returns the raw OSS inventory, not the D1 models table, so freshly uploaded items appear immediately. When to use: you need to enumerate previously uploaded models to find a URN, show an inventory, or pick one for a follow-up tool call. When NOT to use: you already know the exact URN — call get_model_metadata directly. This tool is not a search; it returns up to the OSS default page (typically first 10 objects unless OSS paginates). APS scopes: bucket:read data:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied; 404 bucket not found — no models have been uploaded yet (upload one first); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Idempotent.

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• ai_models

  dynamic-feed

  Current & trending AI MODELS from the open-model ecosystem (Hugging Face) — name, org, task, popularity (likes/downloads) and release date. Use for "what AI models are trending / newest / what's the latest <X> model". This is the OPEN side (Llama, Qwen, DeepSeek, Mistral, Gemma, Phi…); for the closed flagships (GPT, Claude, Gemini, Grok) with pricing & versions use search_ai_models. Args: query: search a model name (e.g. llama, qwen, whisper). org: filter by org/author (e.g. meta-llama, deepseek-ai, Qwen, mistralai, google). task: text-generation (default), text-to-image, automatic-speech-recognition, … or 'any'. sort: trending (default) | newest | downloads. limit: max results. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• picsart_list_models

  Picsart GenAI

  Lists Picsart AI models across ALL modes (image / video / audio / text) and renders the Picsart Studio model-picker widget so the USER can browse, compare, and pick a model visually. Each item carries `id`, `name`, `mode`, `inputType` (and `provider`, `badges`, `description` when `verbose` is true). Use this when the user wants to SEE the available models or pick one themselves — especially when they have not committed to an output mode yet, or for cross-mode searches ("all flux models", "every model with image input"). For known output modes prefer the dedicated tools — `picsart_list_image_models`, `picsart_list_video_models`, `picsart_list_audio_models` — they route better from implicit prompts and need fewer filters. Do NOT use it to fetch a single model's parameter schema (use `picsart_model_params`) or estimate per-call cost (use `picsart_preflight`). If you only need catalog knowledge for your own reasoning (no UI shown to the user), use `picsart_model_catalog` instead. Inputs (all optional): `mode` (filter to image/video/audio/text — text = LLM models that return generated text), `provider` (case-insensitive substring like "flux", "kling", "google"), `acceptsImage` (true → only models that take an image input — i2i, i2v, i2t), `acceptsVideo` (true → only models that take a video input — v2v, v2a, v2t), `acceptsAudio` (true → only models that take an audio input — a2v, sts), `inputType` (exact-match escape hatch; one of t2v/i2v/v2v/a2v/t2i/i2i/t2a/v2a/tts/sts/sfx/music/t2t/i2t/v2t), `limit` (1–100, default 20), `verbose` (default false; when true each item adds provider/badges/description). inputType codes — first letter is input modality, second is output: t2i (text→image), i2i (image→image), t2v (text→video), i2v (image→video), v2v (video→video), a2v (audio→video), t2a (text→audio), v2a (video→audio), tts (text-to-speech), sts (speech-to-speech), sfx (sound effects), music (music gen), t2t/i2t/v2t (LLM text output from text/image/video input). Example: `{ mode: "video", acceptsImage: true, limit: 10 }` returns image-to-video models. Returns `{ items, total, truncated }` — `truncated` is true when more matched than were returned; refine filters or raise `limit` (max 100) to see more. Read-only; spends no credits and works without authentication.

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• picsart_model_catalog

  Picsart GenAI

  Returns the Picsart AI model catalog as plain data — renders NO widget or UI. Use this when YOU (the assistant) need catalog knowledge for your own reasoning: picking a model before `picsart_generate`, answering "which models support X", or comparing options — without pushing a model-picker widget into the conversation. When the user wants to SEE or browse models visually, use `picsart_list_models` instead (it renders the Picsart Studio picker). Same filters and result shape as `picsart_list_models`, but every item is rich by default: `id`, `name`, `mode`, `inputType`, `provider`, `badges`, `description`. Do NOT use it to fetch a single model's parameter schema (use `picsart_model_params`) or estimate per-call cost (use `picsart_preflight`). Inputs (all optional): `mode` (filter to image/video/audio/text — text = LLM models that return generated text), `provider` (case-insensitive substring like "flux", "kling", "google"), `acceptsImage` (true → only models that take an image input — i2i, i2v, i2t), `acceptsVideo` (true → only models that take a video input — v2v, v2a, v2t), `acceptsAudio` (true → only models that take an audio input — a2v, sts), `inputType` (exact-match escape hatch; one of t2v/i2v/v2v/a2v/t2i/i2i/t2a/v2a/tts/sts/sfx/music/t2t/i2t/v2t), `limit` (1–100, default 20), `concise` (default false; when true items carry only id/name/mode/inputType to save tokens). inputType codes — first letter is input modality, second is output: t2i (text→image), i2i (image→image), t2v (text→video), i2v (image→video), v2v (video→video), a2v (audio→video), t2a (text→audio), v2a (video→audio), tts (text-to-speech), sts (speech-to-speech), sfx (sound effects), music (music gen), t2t/i2t/v2t (LLM text output from text/image/video input). Example: `{ mode: "audio", inputType: "music" }` returns music-generation models. Returns `{ items, total, truncated }` — `truncated` is true when more matched than were returned; refine filters or raise `limit` (max 100) to see more. Read-only; spends no credits and works without authentication.

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• picsart_music_studio

  Picsart GenAI

  Opens the Picsart Music Studio: browse music/audio models, compose with a guided prompt builder, generate and play tracks, create AI album-cover art, revisit previously generated tracks, and save everything into a "Music Studio" folder in the user's Picsart Drive. Use when the user wants to MAKE music, a song, a soundtrack, a jingle, or sound effects. Covers text-to-music (MiniMax Music v2, Google Lyria 3 Pro/Clip), short audio clips (Kling T2A), and sound effects (ElevenLabs SFX). Does NOT edit existing audio (no trimming, remixing, or stem work), and is not for text-to-speech / voice cloning or image/video generation. Takes no input. Returns `{ items, total, truncated }` — the curated music catalog the widget renders. Read-only; spends no credits and works without authentication.

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• list_models

  Algenta MCP Server

  List the current Algenta model catalog, including deterministic utility models and any provider-backed routed entries with their routing, failover, timeout, and auth metadata, including capability-specific chat and embedding auth/header readiness. Use this before calling tokenize, count_tokens, chat_completions, responses, embeddings, embedding_similarity, or rerank.

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• generate_video

  BananaBanana Image & Video Generation

  Start an AI video generation (Google Veo 3.1 family or Gemini Omni Flash). EXPENSIVE: $0.10–$4.40 per clip. Cost confirmation is mandatory: the first call always returns a USD quote and charges nothing — repeat the call with confirm_cost set to the quoted amount to actually start. Returns a job_id; poll get_result (videos take 1–10+ minutes). Failed generations are auto-refunded. Models: veo-3.1-fast (default, good quality/price), veo-3.1 (best Veo quality), veo-3.1-lite (cheapest, 720p/1080p), omni-flash (always has sound, model picks 3–10 s duration, flat $1.00, supports conversational editing via edit_from_generation_id). Example: {"prompt": "drone shot over a misty pine forest at sunrise", "model": "veo-3.1-fast", "duration": 8, "resolution": "720p", "confirm_cost": 0.70}

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• create_guides

  serpmantics

  Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query.

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• free2aitools_compare

  Free2AITools

  Compare 2-25 AI catalog entities side-by-side — any catalog entity type (models, datasets, papers, tools), not models only — showing FNI scores, factor breakdown (Semantic, Authority, Popularity, Recency, Quality), specs (params, VRAM, context length) where applicable, and license. USE WHEN you already have 2+ specific entity ids and want a structured side-by-side. DO NOT USE to discover entities, to run/execute a model, or to get a recommendation; the tool presents comparison facts for the caller to decide on, is not an inference router, and returns no paid placement. Read-only, no side effects, no billing. Cold upper-range multi-paper requests may return a transient 503 (retry after the indicated delay). Use free2aitools_select_model or free2aitools_search to discover candidates first, then compare the top ones.

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• openmeteo_get_climate

  open-meteo-mcp-server

  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 (ERA5, 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". 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 DataCanvas for SQL querying when canvas is enabled.

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• picsart_generate

  Picsart GenAI

  Runs any Picsart AI model end-to-end to produce an image, video, audio, or text result. Spends credits. Recommended flow: `picsart_list_models` to pick the model → `picsart_model_params` to learn its inputs → `picsart_preflight` to validate the payload and quote cost → `picsart_generate` to actually run. Do NOT use this for editing operations that have dedicated tools — background removal (`picsart_remove_bg`), background replacement (`picsart_change_bg`), upscale / enhancement (`picsart_enhance`), or raster-to-SVG conversion (`picsart_vectorize`). Also do NOT use it to validate params, quote cost, or browse the catalog — those are separate tools above. Required inputs: `model` (id) and `prompt`. Model-dependent optional inputs: `duration` (video seconds), `aspectRatio` (e.g. "16:9", "9:16", "1:1"), `resolution` (e.g. "1080p", "4k"), `count` (1–8 outputs), `quality`, `style`, `negativePrompt`, `imageUrls` (for image-to-X models), `videoUrl` (for video-to-X), `enhancePrompt`, `generateAudio`, and `extra` — a free-form record for model-specific params (discover them via `picsart_model_params`). Example (image): `{ model: "flux-2-pro", prompt: "a cat in a hat", aspectRatio: "16:9", count: 1 }`. Example (video): `{ model: "kling-v3-pro", prompt: "a cat skiing down a mountain", duration: 5, aspectRatio: "16:9" }`. Returns `{ assets, id, model, created_at, prompt, summary, why_relevant, url, results: [{ url, metadata? }], drive? }` in structured content, plus one `resource_link` block per result URL — image models emit image links, video models emit video links (mime `video/mp4`). `id` is the SDK's generation handle; `metadata` may include model-specific tags (e.g. `exploreImageId` for Recraft Explore models). Text/LLM models (mode "text" in the catalog — e.g. gemini-3-pro, gpt-5.5, claude-*) run synchronously (`async` is ignored) and return the generated text as the text content block plus `text` in structured content. ChatGPT renders images and videos with the Picsart media gallery UI; clients fetch the assets from URLs, never base64. Spends credits and writes to the user's Picsart Drive when the Drive option is enabled. Requires Authorization: Bearer <picsart_token>.

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• lamina_generate_image

  lamina

  Dispatch a single atomic image generation. Sibling of `lamina_create` (the agentic router) — use this when you already know which model fits, or when no app fits the brief. WORKFLOW: (1) `lamina_models_list({ modality: "image" })` → pick a model. (2) `lamina_models_describe({ modelId })` → read its flat `paramSchema`. (3) `lamina_generate_image({ model, prompt, params })` → dispatch, get runId. (4) `lamina_status({ runId, wait: true })` → poll until completed; the response has `output.url`. ONE TOOL, BOTH OPERATIONS: • Text-to-image — call with just `prompt` (and any text-mode params). The model id you picked is the only thing that selects the operation. • Image-to-image (edit / remix / background-swap / etc.) — call the same tool, but include a source image in `params`. Hybrid models (nano-banana-pro, gpt-image-2, gemini-2.5-flash-image, seedream-4.5, flux-2-flex, nano-banana-2, gpt-image-1, gpt-image-1.5) flip to image-to-image automatically when `params.imageUrls` is a non-empty array (or `params.imageUrl` is set for single-source models like flux-pro-kontext). Edit-only models (bria-bg-remove, ideogram-character, ideogram-v3-remix/reframe/replace-background, flux-pro-kontext, ideogram-character-remix) only have image-to-image — `params.imageUrls`/`imageUrl` is required. INPUTS: • `model` (required): a model id from `lamina_models_list`. Don't invent it. • `prompt` (required for most models; check `paramSchema.prompt.required` from `lamina_models_describe`; absent from `paramSchema` for prompt-less models like `bria-bg-remove` and `ideogram-v3-reframe`): natural-language brief; ≤2000 chars. • `params` (model-specific): every key MUST be declared in the chosen model's `paramSchema` (call `lamina_models_describe` first). Unknown keys are rejected with a structured `invalid_params` error; each error has `field` + `allowed`/`range`/`got` so you can correct on retry. Omitted optional keys fall back to schema defaults. • `webhookUrl` (optional): HTTPS URL. On terminal status Lamina POSTs `{runId, status, model, prompt, resolvedParams, output, errorMessage, completedAt}` HMAC-signed. RESPONSE: `{runId, status: "queued"|"completed", model, mode, prompt, resolvedParams}`. `mode` is the resolved value ("text-to-image" | "image-to-image"). The `runId` is the fal_request_id — pass it to `lamina_status`. SYNC vs ASYNC: identical contract. Vertex-backed models (`imagen-4.0-*`, `gemini-2.5-flash-image`) complete in seconds and return `status: "completed"` on the first poll. fal-backed models queue and take 5–60s. `lamina_status({ wait: true })` handles both transparently. ERROR HANDLING: validation failures return `code` + `details.errors[]` with `field` + `error` + `allowed`/`range`/`got`. Common codes: `model_not_supported`, `mode_not_supported`, `invalid_params`, `dispatch_failed`.

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• list_domains

  OpenDataModels MCP server

  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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