"How to make a model aware of the current time before thinking" matching MCP tools:

• faa_search_registrations

  faa-aircraft-registry-mcp-server

  Search active US civil aircraft registrations by owner name, make/model, state, aircraft type, or Mode S (hex) code. Full-text search over the bundled registry; returns decoded summaries with N-numbers to drill into via faa_lookup_registration. At least one filter is required. Owner-name search is unavailable when this deployment redacts owner PII — search by make/model, state, aircraft type, or Mode S code instead. When the result count hits the limit, the response discloses truncation.

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• get_payment_options

  headless-oracle

  Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.

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• check_local_viability

  Local Model Suitability MCP

  Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.

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• check_credit_balance

  mdmagic-mcp-server

  Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline

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• get_current_rates

  CRE Intelligence

  Get live interest rates from the Federal Reserve (FRED). Returns SOFR, 10-year Treasury, 5-year Treasury, Fed Funds Rate, and 30-day SOFR average. Also calculates implied cap rate ranges based on current treasury spreads. Use this BEFORE any DCF model or loan underwriting. These are real-time numbers Claude cannot access on its own.

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• count_subdomains

  Subdomain API

  Return only the total number of distinct subdomains known for a domain — no list. Cheap and low-token. Use when the user asks "how many subdomains" or you only need the size of the attack surface. Count includes historic subdomains that may no longer be live. It is a point-in-time figure that changes over time, so treat it as current-as-of-query, not a fixed value.

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• mu_quote

  mu

  Ask the MU manufacturing router how a thing could be MADE before you create a product: which supplier(s) can make it, the est. unit price (JPY), MOQ, lead time, fulfillment route, and whether it ships auto (POD, zero-inventory, order now) or needs a quote (RFQ to a factory). Pass `kind` (a known POD kind like tee/hoodie/rashguard_ls, OR a non-POD kind like `gi`/`loopwheel_sweat`/`seamless_knit`/`rashguard_premium`) OR a free-text `description` (e.g. "道着 for a dojo", "a seamless knit sweater") and the router infers the kind. Optional `qty`, `region` (e.g. jp/us), `budget` (JPY/unit). Read-only — creates nothing. No API key required. Options are ranked: buyable-now (auto + in budget) first. If an option's mode is `auto`, follow up with mu_create_product; if `quote`, it needs a human RFQ.

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• get_site_files

  Just Publish

  Read the files of a site you already published, so you can make a targeted edit instead of rebuilding the whole site from memory. Returns a complete manifest (every file's path, size, content-type, sha256) plus the contents of the text files (HTML/CSS/JS/etc). Also returns the site's current `version` — pass it back to update_site_file so you don't overwrite a newer change. Pass `paths` to fetch only specific files; omit it to get all text files. Requires site_id + edit_token.

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• sixta_analyze_workload

  sixta-connect

  Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.

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• get_datetime

  local-mcp

  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.

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• get_attributes_for_model

  OpenDataModels MCP server

  List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})

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• check_credit_balance

  mdmagic

  Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline

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• get_capability_spec

  Whatcanido — MCP Server for Service Businesses

  Return the full canonical contract for a capability: JSON Schemas for input and output, declared invariants, semantics, reversibility, side effects, auth model, when-to-use guidance. Plus the list of providers that implement it with current reputation snapshot. Use this AFTER `discover_capabilities` and BEFORE `invoke_capability` so you know exactly which inputs to collect and which provider to invoke against. If you skip this and call invoke_capability with the wrong shape, the response will return missing_fields or schema errors.

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• check_balance

  PikaSim

  Returns the prepaid spending balance of the connected PikaSim agent wallet, in USD. Read-only; makes no changes. Call this before purchase_esim or purchase_phone_plan to confirm sufficient funds, or any time you need the current balance. Requires a connected agent wallet (OAuth or ak_live_ key). If no wallet is connected, the result explains how to connect one.

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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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• predict_liquidation_probability

  MEV Intelligence

  Get a structured liquidation probability prediction for a specific borrower or the top candidates. Returns: health_factor, cross_probability (0–1), estimated_ev_usd, lead_time_estimate_s (how long until crossing at current oracle velocity), oracle_velocity (|Δprice|/min on collateral asset), and a human-readable verdict. This is the core moat tool — it exposes the prediction layer that drives bundle decisions. Call this before submit_bundle to confirm the opportunity is still live and to size your bribe correctly. Full probability model with confidence intervals available at /intelligence/liquidation-waves with x402 payment ($0.50).

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• get_intention

  ManifestYOU

  Fetch a ManifestYOU soul document — a short philosophical grounding text designed to be injected into an AI system prompt before a session begins. Call this at the start of a session to orient the model toward stillness, precision, or creative expansion before work. Paste the returned soul_document into your system prompt or before the first user message.

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• get_cart

  Kifly — Agentic Commerce & Payments

  Inspect the current state of a cart — line items, quantities, prices, and shipping address. Each item includes an `item_id` you can pass to `update_cart_item` to change quantity or remove the item. Call this after `add_to_cart` to review the cart before checkout, or any time the buyer asks what's in the cart. `kifly_purchasable: false` (`fulfillment: "external"`) means checkout on this cart will hand off to the seller's own site instead of charging — say so before the buyer expects a real purchase.

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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• getting_started

  mrmarket.ai

  Onboarding tour for mrmarket.ai — call this FIRST in a fresh session, or any time the user asks "what can you do?" / "how does this work?". Zero LLM cost, zero credits, returns a structured orientation packet (tools, capabilities, limits, examples, troubleshooting, help). Default scope ('overview') covers everything in a short tour. Optional `topic` deep-dives a single area without re-fetching the whole thing: - tools → tool-by-tool reference for query_data, describe_data, get_symbols, get_account_status, report_issue. - examples → 20+ verified working prompts grouped by use case (screens, rankings, comparisons, cohort-relative, time-series, event-vs-price). - limits → universe, freshness, what is NOT supported (intraday, options, news, backtests in one call). - cost → credit model, which tools are free, how to read `credits_remaining`. - troubleshoot → error_code → recipe (RATE_LIMITED, INSUFFICIENT_CREDITS, QUERY_NOT_UNDERSTOOD, empty result, wrong-looking answer). - help → links + how to reach support; preferred channel is `report_issue`. Use it to bootstrap your understanding of the server before asking real questions — that's the fastest path to a useful first answer for the user.

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