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306,895 tools. Last updated 2026-07-27 07:28

"Natural Language to SQL Generation Tools" matching MCP tools:

  • Returns ranked snippets from the AlgoVault knowledge bundle answering a question about its MCP tools, response shapes, integration patterns (LangChain, LlamaIndex, MAF, CrewAI), or code examples. Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes. Fast: BM25 lexical search, no LLM call, no quota cost. For a synthesized natural-language answer use chat_knowledge. Read-only, no side effects.
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  • Search the BLS series catalog by natural language query, survey code, geographic area, or keywords to resolve cryptic SeriesIDs. Returns matching series with decoded components (survey, area, item, seasonal flag) and plain-language names. Use this before bls_get_series when you have a concept but not a SeriesID. Operates offline — no API quota consumed. Survey filter accepts two-letter codes (CU, CE, LN, LA, PC, JT, OE, EC, PR). Area filter accepts state names, MSA names, or FIPS area codes.
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  • Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.5 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy.
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  • Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters — and its bounds, when present — here.
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  • Query the construction project database using natural language (Text-to-SQL). Converts natural language into SQL to retrieve captures, annotations, progress metrics, schedules, and other project records. Pass the user's question as-is without modification. For trade visibility, use `analyze-progress-and-forecasts` instead. **WORKFLOW:** - **Default**: call this tool with only `query`. The server resolves team_domain/facility_key from the saved current project (set via `set-focus-project`). Do NOT call `list-my-projects` again just to obtain these values. - Only when the response indicates the current project is missing, run `list-my-projects` → ask the user → `set-focus-project`, then retry. - Pass explicit team_domain/facility_key **only** when the user clearly wants to query a different project than the saved one. **Available tables:** - progresses: SI progress metrics (level, category, phase, workarea, cost, dates) - captures: Camera captures metadata (level, camera_model, capture_state, user_email) - records: Capture events with timestamps (captured_at, state, id) - photo_notes: Photonotes (description, state, user_email, created_at) - voice_notes: Voicenotes (level, description, state, user_email, created_at) - facilities: Site info (name, address, size, location, bim_count, created_at) - users: User profiles (name, email) - workareas: Spatial zones (level, name, user_name) Args: query: Natural language question (pass as-is, no SQL syntax) team_domain: Omit by default. Pass only to override the current project. facility_key: Omit by default. Pass only to override the current project. user_intent: REQUIRED. Pass the user's original question or request verbatim. Used for analytics only, does not affect results. Returns: List of TextContent with query results and metadata
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  • Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.
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Matching MCP Servers

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    Provides safe, configurable SQL database access via MCP tools, enabling schema introspection, predefined queries, and structured updates with multi-backend support.
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    A secure database query service built on FastMCP framework that allows users to query MySQL databases using natural language, with comprehensive permission management and security controls.
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Matching MCP Connectors

  • FEMA disasters, NOAA weather alerts, USGS earthquakes. 4 tools.

  • Pay-per-call AI tools over x402: web research, summarization, structured extraction (USDC, Base).

  • Get personalized restaurant recommendations based on a natural language query. Uses cuisine, occasion, ambiance, price, and dimensional analysis to find the best matches. Returns ranked results with relevance levels and match reasons in 3-8 seconds. Include a location in your query or provide the location parameter.
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  • Call cc.trade_builder — Takes natural language or structured trade ideas, fetches live market context, and generates fully executable order parameters with AI reasoning. Purpose: Takes natural language or structured trade ideas, fetches live market context, and generates fully executable order parameters with AI reasoning. Behavior: mostly READ (plan/validate). WRITE/destructive only when action=execute — that can submit live orders. Prefer action=plan first. Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.02 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 10/min (per API key). Tier: premium. Returns: Complete trade specification: symbol, side, size, leverage, entry type, TP levels, SL level, AI reasoning for each parameter. Guidelines: Prefer paper/simulation paths. For live money require explicit human confirmation (confirm_live / action=execute). Report real HTTP errors; never invent proxy failures. Tags: ai, trade-planning, order-generation, risk-management, automation.
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  • Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.
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  • Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.
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  • List application guides that show how Blueprint principles apply to engineering challenges (security, evaluation, observability, etc.). Use this to discover which guides exist before drilling in. Prefer guides.search when the user describes a topic or failure mode in natural language. Prefer guides.get when you already know the guide slug and need full detail.
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  • Run a read-only SQL SELECT against tables staged on a DataCanvas by openmeteo_get_historical, openmeteo_get_ensemble, openmeteo_get_flood, or openmeteo_get_climate. Pass the canvas_id returned when any of those tools spills (truncated: true), and reference the exact table_name those tools return alongside it. Call openmeteo_dataframe_describe to list staged tables and their columns when you need to discover names.
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  • Translate a natural-language property-search sentence into a structured filter payload compatible with search_listings. Use this as a transparent intermediate step: pass the user's raw query here, then forward the returned filters — and the returned bounds, when present (they carry the "near <place>" intent) — to search_listings.
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  • Infer search_classifieds filters from a natural-language query. Use this before search_classifieds when the user mentions a category, location, canton or price in free text. Returns a ready-to-use filters object, a category confidence score and warnings for uncertain matches.
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  • MANDATORY FIRST CALL before writing any @marmoui/ui code in this session. Returns a step-by-step generation checklist (which tools to call, in what order), critical rules (no namespace sub-components, PageSection is self-closing, no Sidebar export), component patterns, and ICON LIBRARY RULES. Pass iconLibrary (default "phosphor"; also "material" | "lucide" | "tabler" | "heroicons" | "feather") to get that library's import source, icon name map, and weight/style mapping — and pass the SAME value to review_generated_code so it enforces it. Ask the user which icon library they want before writing UI code. Call topic="patterns" to get the generation checklist specifically.
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  • Get the full plain-language decode of a federal bill by slug (e.g. "hr-2701-119") or citation (e.g. "H.R. 2701" - resolves to the most recent Congress on a match). Returns the AI-generated summary (headline, tl;dr, what/who/why/cost - human-reviewed before publish and clearly labeled when present), the official status in plain language, an urgency band, sponsor, key dates, the official Congress.gov page, and an act_url to Oravan's on-site call flow. This tool never drafts a phone script - script generation only happens on-site, behind a human-review step, never over this API.
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  • START HERE for any custom-print request: the menu of generation choices (print placements with plain-language descriptions, products, backgrounds, engines+costs). Present these to the human — at minimum ask which PLACEMENT and which PRODUCT they want — before calling studio_generate.
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  • List the bundled Hermoso SKILLS — multi-step workflow instructions (SKILL.md) that orchestrate the other tools (research an ad space, plan+render a finished ad, product photoshoot, raw generation) — plus the in-app strategy skills and creative recipes. Call get_skill to load a bundle. Read-only, free.
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  • Ask the directory a customer question in natural language. Returns schema.org cards of businesses that genuinely answer it (LLM-reranked, strictly from the directory — never invented) + grounding links. Use when the customer describes a NEED rather than a search term.
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  • Convert plain-English database questions into working SQL queries — with explanation and optimization notes. Describe what you want to pull from your database and get production-ready SQL. Handles JOINs, aggregations, subqueries, window functions. Use when user says 'write a query to', 'get me all X where Y', 'SQL for', 'how do I query'.
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