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419,788 tools. Last updated 2026-08-09 08:23

"The Models Resource" matching MCP tools:

  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.
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  • 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. 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 DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not.
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  • GET /search — Cross-resource omni-search Cross-resource search across profiles, rooms, messages (incl. private DMs + group DMs you're in), events, and chapters in one round trip. Returns the top-N matches per resource, grouped by resource. Use this when you don't yet know which resource carries the answer — agents typically call this first, then drill into a specific `GET /search/<resource>` for more depth on a single bucket. There's no page param: when you hit the per-resource limit and want more, switch to the per-resource endpoint for that one. The events slice has a baked-in forward-looking default (events ending in the last 30 days or later, and currently enabled) — this matches the in-app "Search across DC" surface. Use `GET /search/events` directly to look further back in time. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.
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  • 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`, plus `supportedAspectRatios`/`supportedResolutions` when the model declares an enum for that param — enough to answer "which models support 16:9" without `picsart_model_params`. Do NOT use it to fetch a single model's FULL 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 plus the ratio/resolution fields, 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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  • 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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  • Run a raw SoQL query against any Los Angeles open-data resource (data.lacity.org) by its Socrata id (8-char like "2nrs-mtv8"). Full SoQL: where/select/group/order/limit/offset. Use la_datasets to find a resource id, or la_recent for the common ones.
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Matching MCP Servers

Matching MCP Connectors

  • The Graph MCP — indexed blockchain data via subgraph GraphQL queries

  • the-committee MCP — wraps StupidAPIs (requires X-API-Key)

  • Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.
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  • 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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  • Run a raw SoQL query against any Cincinnati open-data resource (data.cincinnati-oh.gov) by its Socrata id (8-char like "k59e-2pvf"). Full SoQL: where/select/group/order/limit/offset. Use cincinnati_datasets to find a resource id, or cincinnati_recent for the common ones.
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  • 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.
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  • 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.
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  • Given a deprecated or retiring model, return the provider's recommended replacement(s) and, when none is published, active models from the same provider to consider.
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  • Create a new AI workflow. Just provide ai_model nodes in pipeline order — input/output nodes and connections are auto-generated. For multi-model pipelines, models are wired by matching output→input port types (e.g., model A's image output connects to model B's image input). The original input is shared (fan-out) when multiple models need it. Optionally provide input_files to pre-populate input nodes.
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  • 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).
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  • 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.
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  • Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.
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  • Fetches the top 15 trending HuggingFace models sorted by likes in the last 7 days. Each item includes id (author/name), likes, downloads, pipeline tag, and url. Source: huggingface.co/api/models. Cache TTL 10min. Use when the agent needs to surface what the open-source AI community is paying attention to right now.
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  • Generate text using open-source LLM models hosted on Groq (ultra-fast) or HuggingFace Inference (serverless). No API key required — the server provides its own keys. Supported models: Qwen3 32B, Gemma 4 27B, Gemma 3 27B, Llama 3.3 70B, Llama 4 Scout, DeepSeek R1, Mistral Small 24B, and more. Use list_llm_models to see the full catalog. Rate-limited to prevent abuse.
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