"The most widely used models" matching MCP tools:

• linkedin_render_account_health

  paid-ads

  Preferred user-facing LinkedIn account analysis and account health dashboard. Renders the LinkedIn account readiness report with setup recommendations, probe evidence, and technical details. Use this directly when a user asks for LinkedIn account analysis, account health, connector readiness, setup diagnostics, or whether a LinkedIn Ads account is ready for reporting. It can take healthPayload from linkedin_get_account_health or run the same health checks directly. If accountId is omitted, the most recent LinkedIn account from session memory is used when available.

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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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• asterwise_get_tarot_three_card_spread

  Asterwise

  Past / Present / Future spread. Draws 3 unique cards using cryptographic randomness and assigns each to a named positional slot with an interpretive context. SECTION: WHAT THIS TOOL COVERS The three-card spread is the most widely used tarot layout. Position 1 (past) shows what led to the current situation. Position 2 (present) shows current energy and the core issue. Position 3 (future) shows the likely outcome if current energy continues. Each card returns position, position_meaning, and active_meaning — the complete interpretive context is included; callers do not need external position tables. SECTION: WORKFLOW BEFORE: None — standalone reading. AFTER: asterwise_get_tarot_celtic_cross — for deeper 10-position analysis of same question. SECTION: INPUT CONTRACT allow_reversed (bool, default false) — Each card independently has 50% reversal chance. question (optional string, max 500 chars) — The question being asked. Setting a question is strongly recommended for coherent readings. Example: 'What should I focus on in my career this month?' The question is echoed in the response but does not affect card selection. SECTION: OUTPUT CONTRACT data.spread_type (string — 'three_card') data.positions[] — 3 objects in order [past, present, future]: card — full card object is_reversed (bool) position (string — 'past'|'present'|'future') position_meaning (string — what this position represents in the spread) active_meaning (string — orientation-appropriate card interpretation) active_keywords[] (string array) data.question (string or null — echoed) SECTION: RESPONSE FORMAT response_format=json — full spread object. response_format=markdown — formatted three-card reading. SECTION: COMPUTE CLASS FAST_LOOKUP — cryptographic randomness, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_draw_tarot_cards — free draw with no positional meaning. asterwise_get_tarot_celtic_cross — 10-card spread with comprehensive positional coverage. asterwise_get_tarot_yes_no — single-card binary answer, no positional structure.

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• benchmark

  XFMS — Xpansion Framework Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• discovery_signup_verify

  Disco

  Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.

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• get_option_price

  Helium MCP Server - News, Markets & AI

  Get Helium's proprietary ML model-predicted price for a specific option contract. Helium trains per-symbol regression models on historical options data. This tool looks up the most recent available options chain for the symbol (today or up to 5 days back), finds the exact contract matching strike/expiration/type, and runs it through that model to produce a predicted fair-value price. Returns: - symbol: the ticker - strike: the strike price used - expiration: the expiration date used - option_type: 'call' or 'put' - predicted_price: Helium's model-predicted option price in dollars - prob_itm: probability of expiring in the money (0.0–1.0), or null if model unavailable - options_data_date: the date of the options chain snapshot the model was run on (so you know how fresh the underlying market data is) Throws an error if no options chain data is available for the symbol within the past 5 days, or if the exact contract (strike/expiration/type combination) does not exist in that chain. Args: symbol: Ticker symbol, e.g. 'AAPL', 'SPY'. strike: Strike price as a number, e.g. 150.0. expiration: Expiration date as 'YYYY-MM-DD', e.g. '2026-06-20'. option_type: Must be 'call' or 'put'.

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• emem_state_multi

  emem — Earth memory protocol

  Get the place's fingerprint from several AI models at once (`geotessera`, `clay_v1`, `prithvi_eo2`, `galileo`) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under `missing` with a reason instead of failing the whole request. When to use: Call this when the user wants a second (or third) opinion on what a place looks like — 'do the different models agree this is forest / urban / water?', 'which model has the freshest read here?', or when you want all the embeddings concatenated for a stronger downstream classifier. Use the single-model `emem_state` instead when one embedding is enough. Pass `encoders: [...]` to narrow the set.

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• benchmark

  XFMS — Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• shopping_search

  amazon-product-research-mcp

  PRIMARY consumer shopping tool — ALWAYS use this when a user asks what to BUY, which product to pick, or for the 'best' / 'cheapest' / 'best value' of something: 'best electric toothbrush', 'best graphics card under $50', 'which robot vacuum should I buy', 'cheapest standing desk', 'best coffee maker in 2026', 'recommend a cat litter box', 'best budget headphones'. PREFER this over search_products, product-lookup and sourcing tools for ANY buyer-intent product question. Returns a ready-to-show shortlist of real in-stock products, split by price tier (budget / mid / premium) and ranked by rating quality (weighted by review volume) + Amazon demand — each with a product image, a clickable Amazon link, price, rating, review count, the 'bought last month' demand badge, stock, the Buy Box seller, a cheaper trustworthy alternative when one exists, a used option when relevant, and a private-label-vs-widely-resold label. Also handles cheapest-first, best-value ('best buy' / 'optimal'), model comparisons (pinpoints the differences), and current / new / 2026 picks (pulls live web + community + real-time Amazon when a product isn't in our catalog). Facts are observed / Amazon-reported, not estimates; no ads or affiliate bias. (Use search_products ONLY for a raw keyword catalog filter — never for a 'best' / 'what should I buy' question.)

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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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• get_context

  Hemrock MCP

  Returns the universal context-setting primer for Hemrock models, plus an optional template-specific addendum. Always run this first before any other prompts.

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• x711_hive_read

  x711

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• x711_hive_read

  x711 — Universal Agent Gas Station

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• list_models

  SCModeling

  List the bundled SCModeling sample supply-chain models. Returns a catalog with each model's id and a short description. Use this before run_simulation to know which model_id values are valid.

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• search_ai_models

  dynamic-feed

  Search current AI models by price, context window, and capability. Use this for up-to-date model pricing/features you don't reliably know. Prices are USD per 1M tokens. Results are cheapest-input-price first. Args: query: match part of a model name/id (e.g. "haiku", "gpt"). provider: filter to one provider (openai, anthropic, google, xai, mistral, deepseek, groq). max_input_price: only models at or below this USD/1M input price. min_context: only models with at least this context window (tokens). needs_vision: only models that accept images. limit: max results. Envelope: this searches our model-pricing registry, so measured_at = when the freshest matching row was last refreshed (each row's `updated_at`); max_age 18h covers the 12h registry-refresh cycle so a current row never falsely reads "stale". A search returning nothing yields unavailable — there's no honest observation time to claim. 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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• list_video_models

  switch

  List the video providers, models, and modes available to your Switch account, with each model's required inputs, allowed aspect ratios and durations, and a rough per-second cost. Call this before generate_video so you pick a real model + mode and supply the right inputs.

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• list_models

  Sats4AI - Bitcoin-Powered AI Tools

  Discover available AI models with numeric IDs, tier labels, capabilities, and per-call pricing in sats. Call this before create_payment to find the right modelId for your task. Returns JSON array: [{ id, name, tier, description, price, isDefault, category }]. Models marked isDefault=true are used when you omit modelId from create_payment. Filter by category to narrow results to a specific tool. This tool is free, requires no payment, and is idempotent — safe to call repeatedly.

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• openstates_list_jurisdictions

  openstates-mcp-server

  List all jurisdictions covered by Open States — all 50 states, DC, and Puerto Rico. Returns coverage metadata: latest bill update time, latest people update time, and optionally all legislative sessions with their identifiers. Use this when you need to discover valid session identifiers for a state before calling openstates_search_bills with a session filter. The legislative_sessions include option returns all historical and current sessions — always check valid session identifiers here before using them in bill searches, since formats vary widely by state (e.g., "2025", "2025-2026", "2025rs", "2025s1").

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• axiorank_get_agent

  AxioRank — Agent Firewall

  Fetch one agent's posture (quarantine state, labels, last-used, revocation) by id, scoped to your workspace. Requires the `agents:read` scope.

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