"A tool to query data in Metabase and ask questions" matching MCP tools:

• audiala_contact_teamA

  AudialaA

  Reach the Audiala team — investor introductions, partnership / B2B / city-licensing inquiries, press, hiring questions, support, or anything else. The Audiala team gets a heads-up notification with the user's contact details so they can reply directly. **Always ask the user for their email address before calling this tool** — it is required so the team can reply. Optionally also collect their name and organization. The tool returns the right Audiala mailbox, the founder's LinkedIn, and a suggested intro template the user can adapt if they want to reach out themselves as well.

  ConnectorNo auth

• convoreplyA

  InsideOut (Riley)A

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

  ConnectorNo auth

• get_sampleA

  Cabrini MCPB

  FREE, no payment: real intraday OHLCV bars for AAPL on 2024-01-02, identical in shape to a paid query. Call this first to verify data quality before spending. Takes no arguments — fixed ticker and date.

  ConnectorNo auth

• search_storesA

  PartleA

  Search or list stores in the Partle marketplace. Use for store-led questions ("what hardware shops are in Madrid?") rather than product-led ones (use `search_products` for that). Pass no query to browse the whole catalog. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search over store name and address. Omit to list all stores in default order. limit: Max results (1–50, default 20). Returns: A list of stores with `id`, `name`, `address`, `lat`/`lon` (when geocoded), `homepage`, `type`, and `product_count` (active listings in the store — useful for competitive-landscape sizing without a separate `search_products` round-trip). Pass `id` to `search_products(store_id=…)` to filter the product catalog by that store.

  ConnectorNo auth

• search_storesA

  Partle MarketplacA

  Search or list stores in the Partle marketplace. Use for store-led questions ("what hardware shops are in Madrid?") rather than product-led ones (use `search_products` for that). Pass no query to browse the whole catalog. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search over store name and address. Omit to list all stores in default order. limit: Max results (1–50, default 20). Returns: A list of stores with `id`, `name`, `address`, `lat`/`lon` (when geocoded), `homepage`, `type`, and `product_count` (active listings in the store — useful for competitive-landscape sizing without a separate `search_products` round-trip). Pass `id` to `search_products(store_id=…)` to filter the product catalog by that store.

  ConnectorNo auth

• convoreplyA

  InsideOut (Riley)A

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

  ConnectorNo auth

• list_entitiesA

  TunnelMind Data APIB

  Returns a paginated list of corporate entities in the TunnelMind surveillance database. Includes data categories, estimated data value, and industry classification. Useful for enumerating the surveillance ecosystem by sector. Use this tool when: - You want to enumerate all entities in a specific industry (e.g., all ad-tech companies). - You need a dataset of surveillance entities for analysis or reporting. - You are building a comprehensive surveillance landscape map. Do NOT use this tool when: - You need the full profile of a specific entity — use `get_entity` instead. - You are searching by entity name — use `search` instead. - You need domain-level data — use `list_domains` instead. Inputs: - `industry` (query, optional): Filter by industry classification. Examples: `ad_tech`, `analytics`, `data_broker`, `social`, `crm`. - `limit` (query, optional): Results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from previous response's `next_cursor`. Returns: - Array of entity list items (slug, name, parent_company, industry, data_categories, data_cost_usd). - `meta.has_more` and `meta.next_cursor` for pagination. Cost: - Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page. Latency: - Typical: <150ms, p99: <400ms.

  ConnectorNo auth

• off_search_productsA

  openfoodfacts-mcp-serverA

  Search Open Food Facts by full-text query, structured tag filters, or both at once. Returns a summary list with barcodes, product names, brands, Nutri-Score, NOVA group, and categories — enough for triage and selection, not full label data. Use off_get_product on the returned barcodes for complete details. A text query and tag filters combine: results match the query text and satisfy every filter provided (e.g. query "dark chocolate" with labels_tag "en:organic" and countries_tag "en:france" returns organic chocolate sold in France); additives_tag is the one exception, filtering only on searches with no text query. Tag filter values must be canonical tag IDs (e.g. "en:organic", "en:gluten-free") — use off_browse_taxonomy to resolve human terms to tag IDs. At least one search parameter is required. Data is crowd-sourced; result count reflects contributed products, not all products in the market. Data under ODbL 1.0 — cite Open Food Facts in downstream use.

  ConnectorNo auth

• graphqlA

  StratzB

  Send an arbitrary GraphQL query (with optional variables) directly to the STRATZ API at api.stratz.com/graphql. Use when no named tool covers the data you need. Field names are camelCase and must match the STRATZ schema exactly (e.g. numLastHits, goldPerMinute, constants { gameModes }); a query naming an unknown field fails with "Cannot query field", so introspect the type (`{ __type(name: "MatchPlayerType") { fields { name } } }`) before guessing. Requires a STRATZ Bearer token.

  ConnectorNo auth

• nlqdb_readA

  nlqdb — analytical memory for AI agentsA

  Read-only natural-language query over your agent's memory — SELECT / aggregate / JOIN over existing data. Guaranteed never to write, create, or modify: a request whose plan would change data is refused (use nlqdb_query for that), so this tool is safe to mark 'always allow' in your host. Auto-targets your only database; pass `db` to pick one when you have several. Returns rows + the compiled SQL in trace.

  ConnectorOAuth

• searchA

  ILO Labour Statistics (ILOSTAT)A

  Searches the ILOSTAT labour statistics (≈1,200 SDMX dataflows: employment, unemployment, wages, working time, informality, SDG labour indicators) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `ilo_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.

  ConnectorNo auth

• search_gmail_messagesA

  YouSpotA

  Search the user's connected Gmail mailboxes and return matching messages, newest first — from, to, subject, date, and a short snippet of the body. `query` uses Gmail search syntax: 'in:sent to:jane@acme.com' for messages the user sent to someone, 'from:jane@acme.com' for messages they received, plus operators like subject:, newer_than:7d, and has:attachment. So 'show me the last 5 messages I sent to jane@acme.com' is query 'in:sent to:jane@acme.com' with max_results 5. Each result carries a `starred` flag — a message the user starred matters to them, so weight it accordingly; 'is:starred' finds starred mail directly. For 'how many' questions, use total_matches_estimate in the result — it is Gmail's estimate of ALL matches, beyond the messages returned. If it reports no Gmail account connected, tell the user to connect one at /user/integrations. Rare header-only connections cannot run query search — the tool says so; answer correspondence questions from the brain for those.

  ConnectorNo auth

• compose_landingA

  LayeroA

  Drive the 3-step quiz flow and produce a complete landing. This is the **main entry point**. Don't ask the user questions in chat before calling this — the tool opens native quiz dialogs in the IDE itself. Call this immediately when the user describes what they want. The three quizzes are: 1. **Motivation** — what's being built, for whom, the desired action. 2. **Look & feel** — palette, tone, optional references. 3. **Final picks** — design system (top 3 matched), where submissions go, project name slug. Returns a ``ComposeResult`` with the file bundle to write to disk. The agent then writes the files using the IDE's filesystem tool and proceeds to integration setup / deploy.

  ConnectorNo auth

• connect_analyticsA

  LayeroA

  Connect Yandex Metrika to a site. IMPORTANT: authorisation happens IN A BROWSER, and neither you nor the platform can do that step for the user. The tool returns a link — show it and ask them to open it and grant access. Do not poll in a loop: the person may walk away for an hour. Check later through this same tool without the `branch` argument, or through `site_analytics`.

  ConnectorNo auth

• calibrate_profileA

  RPCS-1 Agent Tuner & Translation BridgeA

  Build a ReceiverProfile (TI, SG, FT, UE, AR — continuous 0-100, never a category label) from five behavioral forced-choice answers. Call with NO answers to get the five questions to ask the user; call again with their answers (a/b/c per primitive) to get the profile. Store the returned profile JSON in the user’s notes or memory and pass it to render_reply / prepare_prompt on every turn. Deterministic and stateless — nothing is stored server-side. Schema: https://rpcs1.dev/v1/receiver-profile.json

  ConnectorNo auth

• list_translationsA

  Tarteel MCP ServerA

  DEFAULT tool for user-facing translation-listing questions. Use this for ANY user-facing query like 'what English translations are available', 'list French translations', 'which translators can I choose from'. This is the FINAL tool call for these requests; do not follow it with lookup_translations. Shows the catalog in an interactive widget the user can browse. Use ISO 639-1 codes like 'en', not names like 'english'. ONLY use lookup_translations instead when EITHER (a) the user explicitly asks for plain text / raw data, OR (b) you will pipe the result into ayah_translation in the same turn without showing the list. When in doubt, use this widget. Returned language_name values are display labels. Rows without usable slugs are filtered out.

  ConnectorNo auth

• formation.guided_setupA

  Admin SubstituteA

  RECOMMENDED ENTRY POINT — Intelligent business formation engine. Provide context about the person (trade, state, qualifications, budget, goals) and get a personalised formation plan with: (1) auto-detected pathway (licensed contractor, fresh start, or investor/operator), (2) task list split into AI-actionable vs human-required, (3) direct government portal links, (4) trade-specific insights (common first jobs, suppliers, rates, growth tips), (5) recommended next tool calls, (6) follow-up questions to ask. Call this FIRST, then use the recommended tool chain for deeper dives.

  ConnectorNo auth

• search_summary_embedsA

  ca-rate-filingsA

  Pure vector search over per-filing extraction-summary embeddings (one embedding per filing, ~59K rows total). Each hit is a filing whose extraction summary is semantically closest to your query, with the matching excerpt and lite filing metadata (state, year, company, product type, filing type, filing date). **Cost**: one query-embedding call + one indexed Postgres lookup. Bounded, cheap, fast. No LLM planning, no LLM composition. Always reach for this before any LLM-driven alternative. **Right surface for *what is this filing about* questions**: - "Show me filings discussing X" — content questions where X is not a concrete filter (wildfire scoring, telematics programmes, autonomous-vehicle exposure, ESG factors, parametric triggers, etc.). - "Find filings that mention <topic>" — when you need to discover filings by content rather than by structured metadata. - "Filings citing trend data on <thing>" — when the question is content-shaped, not numerics-shaped. **Wrong surface for**: - *Actuarial-shape* questions like "filings with credibility under 50%", "filings whose indicated and selected rate diverge sharply", "rate filings where frequency trend is negative". Use `search_actuarial_embeds` — those numerics live in the actuarial memo, not the summary. - Concrete-filter questions like "Filings from carrier NAIC 12345 in 2024" or "ISOF-rooted filings carriers adopted". Use `search_filings` with the typed filters — much faster, no embedding cost at all. - Anything with a SERFF id already in hand — use the `get_filing_*` tools. **How to combine**: - For "recent auto programmes in California with novel rating factors": first `search_filings` (state=CA, product_type="Auto", year_from=…) to get a candidate set, then call this tool over those candidates' descriptions implied by the question. - For "filings whose summary mentions X": this tool alone, then `get_filing_summary` on the top hits to read in full. Returns top-K hits, each with `{serff, similarity, excerpt, meta}`. Default `topK=10`, max 50. Excerpt is the first 800 chars of the matching summary.

  ConnectorNo auth

• arbitrage_historyA

  whale-insightsA

  Historical arbitrage opportunities — top 5 per day (MCP-compatible) — Returns a daily history of the top 5 cross-exchange arbitrage opportunities detected by the platform. Each day entry lists the 5 highest-spread opportunities saved by the cron job, including token symbol, spread percentage, buy/sell exchanges, and average USD volume. Useful for AI agents answering questions like 'which tokens appear most frequently in arbitrage?' or 'what is the average daily spread?'. Data is accumulated daily; older than 180 days is automatically purged. Response: { days, history: [{date, opportunities: [{symbol, spreadPct, buyExchange, sellExchange, usdVolume}]}], total, updatedAt }. Query parameter: ?days=7 (default 7, max 180). No authentication required. 60 requests/min rate limit. 5-min in-process cache. — Use this for daily historical data; use the corresponding live snapshot tool for current conditions and the monthly tool for long-term trends.

  ConnectorNo auth

• recommendA

  Seemor Restaurant IntelligenceA

  Returns a complete, ranked restaurant recommendation for a dining occasion: the final answer, not a search. Each result already carries a grade (or preliminary_band), match_reasons, and caveats weighed against the request. Call it ONCE per question, with the full ask (cuisine, occasion, vibe, constraints) in query and the place in location. Do not re-call with reworded variations or call lookup_restaurant on each result to double-check it; that adds latency, not a better answer. If a result carries caveats, coverage_level 'basic', or a message noting a thin pool, relay it to the user instead of searching again. Requires a location: include one in your query (e.g. "in Soho"), or provide location, or latitude/longitude, or the tool refuses with location_required instead of guessing a city.

  ConnectorNo auth

• store_requestA

  SNHP — free negotiation math + agent memoryA

  Ask for a capability we don't sell yet — free; filings are public and drive what we stock. Two reads in one tool (absorbs the old store_request_status / nextmove_request): pass `request_id` to RE-QUERY a filing's status instead of filing anew — returns {found, request_id, status, status_note, filed_at, door, text} (found: false on an unknown id). Without a request_id it FILES a new ask and returns {request_id, status, watch, check}: every filing is logged verbatim (size-capped, stored as data, never rendered raw) and gets an id you can come back to (GAUNTLET #5). Check any filing with GET /v1/store/request/{id}; the public count is GET /v1/store/requests. Unmet demand decides what gets stocked next — the shelf writes itself from what agents ask for and can't get. Pass watch=True WITH an api_key when filing to flag the ask for a heads-up on a status flip (poll store_my_requests to see it — poll-based, no push); an anonymous watch is ignored, and the chosen flag is echoed as `watch`.

  ConnectorNo auth