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397,341 tools. Last updated 2026-08-05 16:37

"Semantic layer for SQL query execution with dimensions and measures" matching MCP tools:

  • 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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  • LIVE Google Search Analytics query — group by any dimensions (date, page, query, country, device, searchAppearance; up to 3) with page/query filters over up to 16 months of history. Richer than the snapshot tools: use this for ad-hoc analysis. NOTE: including the "query" dimension omits anonymized rare queries — use ["date"] or ["page"] for complete totals on low-traffic sites. Hard cap 100 rows. Read-only.
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  • Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.
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  • Searches Pollar's news archive using semantic and keyword matching. Use for any subject-specific query, including a person, organisation, place, or country (for example, interesting news in Poland). Put the subject or place in query. Locale controls response language, not geographic scope. For current headlines with no subject or place, use list_top_news.
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  • 查询 / 过滤 / 分组聚合数据文件,返回**实际数据行(JSON)**供 AI 直接分析(1 credit/次)。 支持 CSV/TSV/JSON/NDJSON/Parquet,两种用法: · 原始 SQL(表名固定 t):sql="SELECT 商品, sum(销量) s FROM t GROUP BY 商品 ORDER BY s DESC LIMIT 5" · 结构化(不用写 SQL):group_by=["地区"], measures=["销售额"], agg="sum", sort_by="销售额", descending=true, limit=10 SQL 仅允许单条只读 SELECT/WITH,禁止读文件/建表/联网。结果硬上限 1000 行,超出置 truncated=True。失败自动退款。 返回 {ok, format, mode, columns, total_rows, returned_rows, truncated, rows[]}。
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  • Semantic web search powered by Exa. Returns titles, URLs, and the top query-relevant excerpt per result. Compact text by default; pass format='json' for full structured data incl. all excerpts per result. Use glim_web_fetch(url) for full page content. Matching is semantic, so a query with no real match still returns ten nearest-neighbour results rather than zero - judge relevance from the excerpts, not from the result count.
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Matching MCP Servers

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    An MCP server providing SQLite database access for AI agents, enabling SQL execution, schema inspection, CRUD operations, and data export.
    Last updated
    MIT

Matching MCP Connectors

  • AI agents publish bounties for real-world tasks. Gasless USDC payments via x402.

  • Execution Market is the Universal Execution Layer — infrastructure that converts AI intent into physical action. AI agents publish bounties for real-world tasks (verify a store is open, photograph a location, notarize a document, deliver a package). Human executors browse, accept, and complete these tasks with verified evidence (GPS-tagged photos, documents, data). Upon approval, payment is released instantly and gaslessly via the x402 protocol in USDC across 8 EVM chains. Key cap

  • List the tables and column schemas on a DataCanvas staged by an openFDA search tool. Call before openfda_dataframe_query to discover the exact table name, column names, and DuckDB types needed for valid SQL. row_count is the full staged result set, not the inline preview count. Columns typed JSON hold nested openFDA objects/arrays — query them with DuckDB json functions.
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  • Fetch clinical trial results data from ClinicalTrials.gov for completed studies — outcome measures with statistics, adverse events, participant flow, baseline characteristics, and results metadata (limitations & caveats, certain-agreement disclosure restrictions, results point of contact). Only available for studies where hasResults is true. Use clinicaltrials_search_studies first to find studies with results.
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  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
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  • 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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  • Search the Zambo Agent Registry — the public phone book for AI agents. Find agents by capability, name, or description and get back their handle, endpoint URL, wallet address (for x402 payments), and online status. Use this to discover agents that can perform specific tasks, then connect directly or route x402 payments via axis_tool_dispatch (x711 execution layer). Free, no auth, unlimited searches.
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  • PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.
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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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  • Use ONLY when the query consists entirely of explicit numeric/categorical constraints with NO descriptive language (no mood, view, atmosphere, or aesthetic words). Returns rating-sorted (or price-sorted) results from SQL filter without semantic ranking. For ANY query containing descriptors like 'cozy', 'quiet', 'luxury', 'river view', 'modern', use search_rentals_natural instead — it produces better results in a single call. Returns Schema.org Accommodation format.
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  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
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  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
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  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
    Connector
  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
    Connector
  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
    Connector