"Natural Language to SQL Conversion and Executing MySQL Queries to Retrieve Data" matching MCP tools:

• noaa_climate_find_locations

  noaa-climate-mcp-server

  Search for geographic locations by category (CITY, ST, CNTY, CNTRY, ZIP, CLIM_REG, etc.). Returns location IDs used in station search and data queries. Without locationCategoryId, returns all location types. Use locationCategoryId=ST to list US states (51 entries — small enough to retrieve completely). Use locationCategoryId=CITY for cities (thousands of entries — use pagination and sortField=name to navigate alphabetically). The CDO API has no name-search parameter; to find a specific city, sort alphabetically with sortField=name and page through results. Location IDs: states as FIPS:37 (NC), cities as CITY:US530031, zip codes as ZIP:98101, countries as CNTRY:US. Obtain location IDs here, then pass them to noaa_climate_find_stations or noaa_climate_fetch_data.

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• vibekit_db_query

  VibeKit

  Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.

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• advanced_search

  Sidecar

  Search for data assets using identifier matching, keyword search, semantic similarity, or hybrid fusion. Use 'identifier' mode (default) when you know the asset name or a pattern (supports * wildcards). Use 'keyword' when searching by domain terms or metadata keywords. Use 'semantic' for natural language queries about what the data represents. Use 'hybrid' to combine all approaches for the best recall. Returns a ranked list of matching assets with identifiers, types, and relevance scores.

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• psych_cta-score

  Tollmint

  Score call-to-action text against a curated behavioural-science corpus. Checks verb framing (outcome versus effort), friction words introduced at the point of commitment, hedging, ownership framing, and length — each finding citing the effect it rests on and that effect's replication status, so a recommendation grounded in a contested result is visibly weaker than one grounded in a robust one. Separately flags urgency language the surrounding copy does not support, which is a regulatory exposure rather than merely a conversion cost. Deterministic: the same text scores identically every time, so two variants can be compared without sampling noise. Does not predict a conversion lift, because the corpus does not support that precision. Costs $0.012000 per call.

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• fema_dataframe_describe

  fema-mcp-server

  List tables and column schemas on a DataCanvas staged by fema_search_nfip. Call this before fema_dataframe_query to discover the exact table name, column names, and DuckDB data types needed to write valid SQL. Row count reflects what was actually staged — check truncated in the fema_search_nfip response to know whether the canvas holds the full matching set.

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• list_events

  ClearMarket

  Browse or search ClearMarket prediction-market events. Filter by category, platform, Resolution Clarity Grade, or free-text `q`. `q` is token-AND across question + tags, so SHORT KEYWORD queries match best ("microstrategy bitcoin", "fed rate") — natural-language phrases often return nothing. Returns compact graded summaries: slug, question, venues_covered, primary grade, rcg_score (0-100, for ranking clarity), last_price, and status (open / resolved). Start here when you have a topic but not a slug; then call get_event for the full graded record. Categories: economics, financials, crypto, companies, technology, politics, geopolitics, health, climate.

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• search_listings

  SPACEXPLORATION

  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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• tickerbot_explain_scan

  mcp-server

  Dry-run a scan query WITHOUT executing it: validates the SQL, shows the compiled predicate after custom-signal expansion, resolves universe scope and (for asof) the serving tier, and returns the planner cost/row estimate. Always returns 200 — failures come back as { valid: false, stage, error, message }. Use before subscribing a query or to debug invalid_query errors.

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• precis_orientation

  Précis Finance MCP

  Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.

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• interpret_search

  SPACEXPLORATION

  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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• quote

  FlowVolt

  Direct quote path. Use when the user already knows who they want to reach and just asks for pricing or to find leads. Takes a natural-language description and returns six plans in plain prose. For users who haven't expressed their ICP yet, prefer discover_business which tailors the response. Pass `name` and `company` if you know them.

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• validate_sql

  sqlai.dev SQL Verifier

  Validate a SQL query against a schema WITHOUT executing it (parse + name/type binding via EXPLAIN). Returns ok with referenced tables, or a structured error: {type: unknown_column|unknown_table|syntax|..., message, position, suggestion}. The suggestion is rule-based (edit distance against your schema). Example: query "SELECT nmae FROM users" → error type unknown_column, suggestion 'did you mean "name"?'.

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• search_workspaces

  FrankSpace Public MCP

  Search live UK workspace listings on FrankSpace. Filter by location text (city, postcode, submarket), size band, and maximum monthly price (pence). For richer natural-language queries prefer `ai_search`.

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• search_bookmarks

  linkedmash

  Search through a user's LinkedIn bookmarks using either keyword (text) search or semantic (meaning-based) search. Supports all list_bookmarks filters, full-text search via the q field, and semantic search via the vector_search_term field for natural language or topic-based queries. IMPORTANT: At least one of 'q', 'vector_search_term', or 'author' must be provided. If the user asks to find posts by a specific author (e.g., 'Show posts by John Doe', 'Find posts from Suresh sambantham'), use the 'author' parameter instead of putting the author name in the 'q' field. Use 'q' for searching post content, not for filtering by author.

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• scalix_db_text_to_sql

  Scalix Cloud

  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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• polybridge_search

  PolyBridge

  Search public prediction markets for markets relevant to a natural-language topic or question. Use when you need candidate markets, market URLs, outcomes, statuses, and relevance scores. Do not use for a final probability forecast, market-history lookup, trading, or private/internal data. Scores are relevance scores, not probabilities.

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• search

  Pollar News

  Searches Pollar's published news and returns a ranked list of matching articles as {id, title, url}. Use for queries about a subject, person, organisation, place, or country; put that subject or place in query. Locale controls response language, not geographic scope. Pass an id to fetch to read the article.

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• director_note

  framesail

  Edit ONE scene with a natural-language note (the same director chat the editor UI uses): move/restyle/add/remove layers and overlays, retime, etc. Synchronous — returns the applied mutations + updated scene. Use list_scenes to find scene ids; for notes spanning the whole video use project_director_note instead.

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• query_data

  Algenta MCP Server

  Execute a structured query against connected data sources. Convert the user's question to a structured intent and call this tool — do NOT try to write SQL or parse column names yourself. The engine resolves column meaning from mathematical relationships and statistical structure only. It works on any dataset without configuration. The governed filter shape is a record-predicate contract over normalized rows, not a SQL predicate language, so it also applies to Redis and other non-SQL sources. Structural roles (use in metric.role): - derived_measure: the main financial/operational aggregate (revenue, spend, value) - base_measure: counts, quantities, discrete amounts - unit_measure: per-unit prices, rates - ratio: percentages, margins, fill rates (0-1 range) - metric: let the engine pick the best numeric column If clarification_required is true, or if confidence < 0.85, check the candidates list and ask the user to clarify. Never fabricate column names or SQL.

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• sync_stripe_conversions

  freedom-mcp

  Record won deals from the company's connected Stripe so lead→paid conversion becomes measurable. Reads paid Stripe customers (read-only), matches them to leads by email, and records a closed_won deal per paying customer (idempotent — re-running is safe, never double-counts). Only works if Stripe is connected. Use when conversion "isn't measured yet" or to refresh the conversion picture. Routing: CRM/sales/revenue → measure conversion / record won deals from Stripe → use this [write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

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