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650,472 tools. Updated 2026-10-13 11:41

"Importing Excel Data into SQL Server" 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.
    ConnectorNo auth
  • Run a read-only SQL SELECT against water data tables staged on a DataCanvas by water_get_series or water_find_sites. Workflow: run water_get_series or water_find_sites (get canvas_id + table_name) → water_dataframe_describe (confirm the table and its columns) → water_dataframe_query (SQL analysis), then optionally water_dataframe_drop (when this server enables it) to remove a table you are done with. Only SELECT statements are permitted. At most 10,000 rows are returned; a query matching more is capped and the response sets truncated=true — scope with WHERE/LIMIT, and use SELECT COUNT(*) or water_dataframe_describe to learn the true match count. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.
    ConnectorNo auth
  • Describe what you want done to a file in plain language — e.g. "translate this contract to German", "pull every table out of this PDF into Excel", "shrink this video to under 25MB", "convert this to PDF". The instruction is routed to the right job automatically; if the request is not supported yet you get an honest explanation of what is. Provide EITHER source_url OR base64_content, plus a filename with extension.
    ConnectorNo auth
  • List the SQL databases (D1 or Neon Postgres) on my account, including which owned site (if any) each is attached to. Call this BEFORE db_query/db_schema-style work to discover a databaseId — those live on a per-database MCP server reached via GET /api/v1/databases/{id} (see llms.txt), which this id feeds.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • Order a formatted, downloadable financial report (Excel or PDF) for a Norwegian company — the same multi-source-fused figures, layout and source note as the report a customer would download in the Firmaradar portal, ready to file or forward. Returns a short-lived download link + metadata (years covered, source, currency), NOT the file itself and NOT base64 data — fetch the download_url separately, no further auth required, within expires_in seconds. Use `get_company_financials` instead when you need the raw figures to reason about, not a document to hand off. Requires the Excel-export or PDF-export add-on (matching the requested format) on the caller's account. Charges 1 credit per financial year included in the report.
    ConnectorNo auth

Matching MCP Servers

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    quality
    D
    maintenance
    A custom MCP server that gives Claude Desktop direct access to a SQLite database, enabling natural language questions to be answered through real SQL execution.
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  • F
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    quality
    C
    maintenance
    Enables MCP clients to create and manipulate Excel workbooks programmatically, including sheets, cell data, formatting, formulas, charts, and pivot tables.
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Matching MCP Connectors

  • Last resort for ad hoc questions the other tools cannot answer. Takes a plain-English question, generates SQL over NEMPulse's tables, and returns the SQL, the result rows and a short explanation. It is the slowest tool (can take close to a minute) and it is AI-generated, so check the returned SQL before relying on a number. Scope each question to about one region-month or less: wider aggregates (a full year by region, or per-day top-N across all regions) risk exceeding the 8 second SQL limit, and a longer wait does not help. For per-day top-N or bottom-N questions, ask for a window function (ROW_NUMBER or RANK) rather than a per-day subquery, which has been seen to silently return all-null rows. Queryable data: dispatch prices, daily revenue, optimal dispatch, battery price profiles and market events. The market analysis tables (monthly spreads, regressions, correlations) are not reachable here.
    ConnectorNo auth
  • PHP + MySQL plans: runs one read-only SQL statement (SELECT, SHOW, DESCRIBE, EXPLAIN or WITH … SELECT) against the site's database and returns the rows. Runs in a read-only transaction; use execute_sql to change data. Use ? placeholders with params for values.
    ConnectorOAuth
  • Export a generated dataset as a file. Returns a `download_url` the person can open (or you can fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link rather than pasting file contents into the chat. Args: dataset_id: from a prior generate_dataset call. format: data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql. code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy, typescript, jsonschema, expectations, django, openapi, mockapi, demo. `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) — the way to seed a real database: run the returned SQL through your own database connection, since this server never holds a database credential itself. dialect: for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake. inline: also return the file itself as `base64` (only for files under a few MB). Use it when you must write the file yourself and cannot fetch a URL. Returns: filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when `inline` and small enough.
    ConnectorNo auth
  • Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.
    Connector
    Destructive
    No auth
  • Gera um relatório do CIGAM (operação LENTA, ~2 min, produz um arquivo Excel). Espera inline até `aguardar_segundos`; se não ficar pronto, retorna um `job_id` (busque depois com cigam_job, sem ficar repetindo). Retorna `arquivo_url` (link temporário do Excel). Ex. de report_id: REL_201 (Vendas por Período), REL_610 (posição atual de estoque). Bulk support: accepts report_ids for batched execution.
    ConnectorNo auth
  • Create a new data source from an inline base64-encoded file (CSV, TSV, JSON, Excel, TXT, PDF). The file goes through the same validation and preprocessing as a web upload. Returns the data_source_id you can pass to run_analysis as soon as preprocessing completes (poll get_data_source_schema for readiness or pass wait_seconds to block here).
    ConnectorNo auth
  • Build an Excel (.xlsx) workbook from a sheet spec: columns with keys, row objects keyed to those columns, optional Excel number formats and a formula-aware total row (bare 'SUM' / 'AVG' / 'COUNT' / 'MIN' / 'MAX' expands into a real formula over the column's data range). Choose this over render_pdf when the recipient will sort, filter or recompute the numbers, and over render_docx when the content is tabular rather than prose. Returns a stored render { id, url, bytes, durationMs, format } where url is a signed download link valid for one hour; the workbook is a .xlsx, so feed the id to convert_document if the next step needs a PDF. Header rows are always bold on a tinted fill — there is no flag for it. Counts one render against the monthly quota. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.
    ConnectorNo auth
  • Build an Excel (.xlsx) workbook from a sheet spec: columns with keys, row objects keyed to those columns, optional Excel number formats and a formula-aware total row (bare 'SUM' / 'AVG' / 'COUNT' / 'MIN' / 'MAX' expands into a real formula over the column's data range). Choose this over render_pdf when the recipient will sort, filter or recompute the numbers, and over render_docx when the content is tabular rather than prose. Returns a stored render { id, url, bytes, durationMs, format } where url is a signed download link valid for one hour; the workbook is a .xlsx, so feed the id to convert_document if the next step needs a PDF. Header rows are always bold on a tinted fill — there is no flag for it. Counts one render against the monthly quota. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.
    ConnectorNo auth
  • Upsert leads from an uploaded CSV or XLSX file into this account. WRITES TO THE DATABASE — SENDS NOTHING on any channel, ever; this only stores contacts. Deduplicates by phone/email (dedupe_by, default both) against both the file itself and every lead already in this account. Re-importing a lead that is opted-out, suppressed, or do-not-contact NEVER clears those flags and NEVER moves the lead — only genuinely new contacts are created. Returns an import_id: pass it to clone_campaign_draft's import_ids to attach this audience to a campaign. idempotency_key is required — retrying the same key returns the same result rather than importing twice; reusing it with different arguments is rejected as idempotency_conflict.
    ConnectorOAuth
  • PDF to Excel Inspector — Non-destructive scan of a PDF's tables before converting: per-table row/column counts, confidence flags, warnings. [category: pdf]
    ConnectorOAuth
  • Find observation variables (traits) by name, trait class, ontology term, or free-text query. Free-text queries are ranked against the returned set and may resolve to ontology URIs when the server advertises them. When the upstream total exceeds loadLimit, the full result set is materialized as a dataframe — query it with brapi_dataframe_query (SQL).
    ConnectorNo auth
  • Read-only SQL over a dataset's own tables (one SELECT/WITH statement; every table name is a view over that dataset's own files — nothing else on the server is reachable this way). Args: dataset_id: from a prior generate_dataset call. sql: a single SELECT (or WITH ... SELECT) statement. No semicolons, file paths, or statements that write or read outside the dataset (checked before running). limit: rows returned (capped at 5000). Returns: columns, rows, truncated (whether more rows existed than `limit`).
    ConnectorNo auth
  • Check an audit started with start_audit: returns overall status, per-step progress (scan → viewports → screenshots → agent → excel → publish), and — once complete — a severity summary plus short-lived download URLs for the report (JSON) and the Excel workbook. Poll every ~15s while status is pending/running. Only the account that started an audit can read it. Free with a WebAbility account.
    ConnectorNo auth
  • Replay VRP dashboard (z-score, percentile, regime, strategy scores) at any minute since April 2018. Percentiles and z-scores are leak-free: date-bounded in SQL so the backtest only sees data strictly before the `at` timestamp. Alpha tier.
    ConnectorNo auth
  • PHP lint: POST {code}, get the bugs back with line numbers. Deterministic static analysis — code is never executed, no AI. Catches SQL injection (request data interpolated into query strings), the removed mysql_* API, unbalanced braces, '+' string concat, = vs == in conditions, type-juggling loose comparisons, eval/extract on request data, unused variables, leftover var_dump. Understands PHP tags, heredocs, interpolation. Max 128 KB. ($0.002 per call, paid via x402)
    ConnectorNo auth